scientific experiments include these changeable parts

• Activities, Experiments, Online Games, Visual Aids
• Activities, Experiments, and Investigations
• Experimental Design and the Scientific Method

Experimental Design - Independent, Dependent, and Controlled Variables

To view these resources with no ads, please login or subscribe to help support our content development. school subscriptions can access more than 175 downloadable unit bundles in our store for free (a value of $1,500). district subscriptions provide huge group discounts for their schools. email for a quote: [email protected] ..

Scientific experiments are meant to show cause and effect of a phenomena (relationships in nature).  The “ variables ” are any factor, trait, or condition that can be changed in the experiment and that can have an effect on the outcome of the experiment.

An experiment can have three kinds of variables: i ndependent, dependent, and controlled .

• The independent variable is one single factor that is changed by the scientist followed by observation to watch for changes. It is important that there is just one independent variable, so that results are not confusing.
• The dependent variable is the factor that changes as a result of the change to the independent variable.
• The controlled variables (or constant variables) are factors that the scientist wants to remain constant if the experiment is to show accurate results. To be able to measure results, each of the variables must be able to be measured.

For example, let’s design an experiment with two plants sitting in the sun side by side. The controlled variables (or constants) are that at the beginning of the experiment, the plants are the same size, get the same amount of sunlight, experience the same ambient temperature and are in the same amount and consistency of soil (the weight of the soil and container should be measured before the plants are added). The independent variable is that one plant is getting watered (1 cup of water) every day and one plant is getting watered (1 cup of water) once a week. The dependent variables are the changes in the two plants that the scientist observes over time.

Can you describe the dependent variable that may result from this experiment? After four weeks, the dependent variable may be that one plant is taller, heavier and more developed than the other. These results can be recorded and graphed by measuring and comparing both plants’ height, weight (removing the weight of the soil and container recorded beforehand) and a comparison of observable foliage.

Using What You Learned: Design another experiment using the two plants, but change the independent variable. Can you describe the dependent variable that may result from this new experiment?

Think of another simple experiment and name the independent, dependent, and controlled variables. Use the graphic organizer included in the PDF below to organize your experiment's variables.

Please Login or Subscribe to access downloadable content.

Citing Research References

When you research information you must cite the reference. Citing for websites is different from citing from books, magazines and periodicals. The style of citing shown here is from the MLA Style Citations (Modern Language Association).

When citing a WEBSITE the general format is as follows. Author Last Name, First Name(s). "Title: Subtitle of Part of Web Page, if appropriate." Title: Subtitle: Section of Page if appropriate. Sponsoring/Publishing Agency, If Given. Additional significant descriptive information. Date of Electronic Publication or other Date, such as Last Updated. Day Month Year of access < URL >.

Here is an example of citing this page:

Amsel, Sheri. "Experimental Design - Independent, Dependent, and Controlled Variables" Exploring Nature Educational Resource ©2005-2024. March 25, 2024 < http://www.exploringnature.org/db/view/Experimental-Design-Independent-Dependent-and-Controlled-Variables >

Exploringnature.org has more than 2,000 illustrated animals. Read about them, color them, label them, learn to draw them.

Scientific Method Example

Illustration by J.R. Bee. ThoughtCo. 

• Cell Biology
• Weather & Climate
• B.A., Biology, Emory University
• A.S., Nursing, Chattahoochee Technical College

The scientific method is a series of steps that scientific investigators follow to answer specific questions about the natural world. Scientists use the scientific method to make observations, formulate hypotheses , and conduct scientific experiments .

A scientific inquiry starts with an observation. Then, the formulation of a question about what has been observed follows. Next, the scientist will proceed through the remaining steps of the scientific method to end at a conclusion.

The six steps of the scientific method are as follows:

Observation

The first step of the scientific method involves making an observation about something that interests you. Taking an interest in your scientific discovery is important, for example, if you are doing a science project , because you will want to work on something that holds your attention. Your observation can be of anything from plant movement to animal behavior, as long as it is something you want to know more about.​ This step is when you will come up with an idea if you are working on a science project.

Once you have made your observation, you must formulate a question about what you observed. Your question should summarize what it is you are trying to discover or accomplish in your experiment. When stating your question, be as specific as possible.​ For example, if you are doing a project on plants , you may want to know how plants interact with microbes. Your question could be: Do plant spices inhibit bacterial growth ?

The hypothesis is a key component of the scientific process. A hypothesis is an idea that is suggested as an explanation for a natural event, a particular experience, or a specific condition that can be tested through definable experimentation. It states the purpose of your experiment, the variables used, and the predicted outcome of your experiment. It is important to note that a hypothesis must be testable. That means that you should be able to test your hypothesis through experimentation .​ Your hypothesis must either be supported or falsified by your experiment. An example of a good hypothesis is: If there is a relation between listening to music and heart rate, then listening to music will cause a person's resting heart rate to either increase or decrease.

Once you have developed a hypothesis, you must design and conduct an experiment that will test it. You should develop a procedure that states clearly how you plan to conduct your experiment. It is important you include and identify a controlled variable or dependent variable in your procedure. Controls allow us to test a single variable in an experiment because they are unchanged. We can then make observations and comparisons between our controls and our independent variables (things that change in the experiment) to develop an accurate conclusion.​

The results are where you report what happened in the experiment. That includes detailing all observations and data made during your experiment. Most people find it easier to visualize the data by charting or graphing the information.​

Developing a conclusion is the final step of the scientific method. This is where you analyze the results from the experiment and reach a determination about the hypothesis. Did the experiment support or reject your hypothesis? If your hypothesis was supported, great. If not, repeat the experiment or think of ways to improve your procedure.

• Glycolysis Steps
• Biology Prefixes and Suffixes: chrom- or chromo-
• Biology Prefixes and Suffixes: proto-
• 6 Things You Should Know About Biological Evolution
• Biology Prefixes and Suffixes: Aer- or Aero-
• Taxonomy and Organism Classification
• Homeostasis
• Biology Prefixes and Suffixes: diplo-
• The Biology Suffix -lysis
• Biology Prefixes and Suffixes Index
• Biology Prefixes and Suffixes: tel- or telo-
• Parasitism: Definition and Examples
• Biology Prefixes and Suffixes: Erythr- or Erythro-
• Biology Prefixes and Suffixes: ana-
• Biology Prefixes and Suffixes: phago- or phag-
• Biology Prefixes and Suffixes: -phyll or -phyl

Sciencing_Icons_Science SCIENCE

Sciencing_icons_biology biology, sciencing_icons_cells cells, sciencing_icons_molecular molecular, sciencing_icons_microorganisms microorganisms, sciencing_icons_genetics genetics, sciencing_icons_human body human body, sciencing_icons_ecology ecology, sciencing_icons_chemistry chemistry, sciencing_icons_atomic &amp; molecular structure atomic & molecular structure, sciencing_icons_bonds bonds, sciencing_icons_reactions reactions, sciencing_icons_stoichiometry stoichiometry, sciencing_icons_solutions solutions, sciencing_icons_acids &amp; bases acids & bases, sciencing_icons_thermodynamics thermodynamics, sciencing_icons_organic chemistry organic chemistry, sciencing_icons_physics physics, sciencing_icons_fundamentals-physics fundamentals, sciencing_icons_electronics electronics, sciencing_icons_waves waves, sciencing_icons_energy energy, sciencing_icons_fluid fluid, sciencing_icons_astronomy astronomy, sciencing_icons_geology geology, sciencing_icons_fundamentals-geology fundamentals, sciencing_icons_minerals &amp; rocks minerals & rocks, sciencing_icons_earth scructure earth structure, sciencing_icons_fossils fossils, sciencing_icons_natural disasters natural disasters, sciencing_icons_nature nature, sciencing_icons_ecosystems ecosystems, sciencing_icons_environment environment, sciencing_icons_insects insects, sciencing_icons_plants &amp; mushrooms plants & mushrooms, sciencing_icons_animals animals, sciencing_icons_math math, sciencing_icons_arithmetic arithmetic, sciencing_icons_addition &amp; subtraction addition & subtraction, sciencing_icons_multiplication &amp; division multiplication & division, sciencing_icons_decimals decimals, sciencing_icons_fractions fractions, sciencing_icons_conversions conversions, sciencing_icons_algebra algebra, sciencing_icons_working with units working with units, sciencing_icons_equations &amp; expressions equations & expressions, sciencing_icons_ratios &amp; proportions ratios & proportions, sciencing_icons_inequalities inequalities, sciencing_icons_exponents &amp; logarithms exponents & logarithms, sciencing_icons_factorization factorization, sciencing_icons_functions functions, sciencing_icons_linear equations linear equations, sciencing_icons_graphs graphs, sciencing_icons_quadratics quadratics, sciencing_icons_polynomials polynomials, sciencing_icons_geometry geometry, sciencing_icons_fundamentals-geometry fundamentals, sciencing_icons_cartesian cartesian, sciencing_icons_circles circles, sciencing_icons_solids solids, sciencing_icons_trigonometry trigonometry, sciencing_icons_probability-statistics probability & statistics, sciencing_icons_mean-median-mode mean/median/mode, sciencing_icons_independent-dependent variables independent/dependent variables, sciencing_icons_deviation deviation, sciencing_icons_correlation correlation, sciencing_icons_sampling sampling, sciencing_icons_distributions distributions, sciencing_icons_probability probability, sciencing_icons_calculus calculus, sciencing_icons_differentiation-integration differentiation/integration, sciencing_icons_application application, sciencing_icons_projects projects, sciencing_icons_news news.

• Share Tweet Email Print
• Home ⋅
• Science Fair Project Ideas for Kids, Middle & High School Students ⋅

5 Components of a Well-Designed Scientific Experiment

How to Write a Summary on a Science Project

Whether in middle school or a laboratory at NASA, the scientific method is the accepted approach for conducting an experiment. The five components of the scientific method are: observations, questions, hypothesis, methods and results. Following the scientific method procedure not only ensures that the experiment can be repeated by other researchers, but also that the results garnered can be accepted.

Observations and Question

Observations allow an experimenter to gather and use background information concerning the principles being tested to better predict and understand the forthcoming outcome. A researcher or student may choose to perform independent research or look at similar experiments before making observations. The question is the aspect being tested, what is the experiment is trying to answer. For example, the question that an experiment may ask is: "Does the temperature of ice increase as it undergoes a phase change?"

The hypothesis is a prediction of the outcome, which is generally stated in a complete sentence; it uses the observations made before the experiment to make an educated assertion. At the end of the experiment, the researcher will have to use the results to decide if he can accept the hypothesis or reject it. The hypothesis must stand up to questioning during the experiment.

The method section of the scientific method lists all of the materials used in the experiment in specific detail along with the exact procedures that were taken. It is important that the methods are detailed and accurate so another researcher can repeat the experiment and expect to get similar results. It is also necessary to list the methods used because it may be useful to go back to them after the experiment to explain some of the results that occurred.

You must record the results of the experiment. Researchers must interpret the results they receive, giving explanations for the data gathered. Most importantly, they must also draw a conclusion from the results. The conclusion must decide whether to accept or reject the hypothesis made at the beginning of the experiment. It is often useful to display results with visual aids, such as graphs or charts, to help identify trends and relationships.

Related Articles

What are the 8 steps in scientific research, how to calculate experimental value, steps & procedures for conducting scientific research, difference between proposition & hypothesis, what is the next step if an experiment fails to confirm..., five characteristics of the scientific method, why should you only test for one variable at a time..., definitions of control, constant, independent and dependent..., scientific method for volcano science projects, methods of probability, what is the meaning of variables in research, 10 characteristics of a science experiment, science project ideas & the scientific method, how to write a protocol for biology experiments, why is constant temperature important in an experiment, how to do a science project step-by-step, how to write a hypothesis for correlation, the scientific method for an egg drop, difference between data & conclusion of a study.

• University of Rochester Department of Physics and Astronomy; Introduction to the Scientific Method; Prof. Frank L. H. Wolfs

About the Author

Clayton Yuetter has worked as a professional writer since 1999. His writing has appeared in many journals and websites such as The Milk House, The Country Folks, Progressive Dairyman and Three Times Daily. He received a Master of Arts in writing at the National University of Ireland, Galway.

Photo Credits

Photos.com/AbleStock.com/Getty Images

Find Your Next Great Science Fair Project! GO

• Skip to main content
• Keyboard shortcuts for audio player

13.7 Cosmos & Culture

What makes science science.

Tania Lombrozo

In a post published last week, Adam Frank argued for the importance of public facts, and of science as a method for ascertaining them.

He emphasized the role of agreement in establishing public facts, and verifiable evidence as the crucial ingredient that makes agreement possible.

Today, I want to consider two additional aspects of science as a method for ascertaining public facts — that is, the facts that we should all accept together. The first is that scientific conclusions can change. And the second is that scientific methods can change.

Far from undercutting the value of public facts, understanding how and why these changes occur reveals why science is our best bet for getting the facts right.

First, the body of scientific knowledge is continually evolving. Scientists don't simply add more facts to our scientific repository; they question new evidence as it comes in, and they repeatedly reexamine prior conclusions. That means that the body of scientific knowledge isn't just growing, it's also changing.

At first glance, this change can be unsettling. How can we trust science, if scientific conclusions are continually subject to change ?

The key is that scientific conclusions don't change on a whim. They change in response to new evidence, new analyses and new arguments — the sorts of things we can publicly agree (or disagree) about, that we can evaluate together. And scientific conclusions are almost always based on induction, not deduction. That is, science involves drawing inferences from premises to conclusion, where the premises can affect the probability of the conclusions but don't establish them with certainty.

When you put these pieces together, the alternative to an evolving body of scientific knowledge is a non-starter. To embrace a static body of scientific knowledge is to reject the potential relevance of new information. It's a commitment to the idea that a conclusion based on all the evidence available is no better than a conclusion based on the subset of evidence we happened to obtain first. If a changing body of scientific knowledge is unsettling, this alternative is untenable.

A second feature of science is that scientific methods are continually evolving. Many of us learned "the" scientific method in grade school, a step-by-step procedure for doing science. But this recipe-book approach to science is oversimplified and misleading . Scientists employ a variety of methods, and these methods are refined as we learn more. New technologies, like telescopes or brain imaging devices, allow us to ask new questions in new ways. But equally important, strategies for analyzing data and drawing conclusions change as well. Statistical methods improve, as do experimental designs. The randomized controlled trial is a scientific innovation; a way to draw better conclusions about cause and effect. A double-blind experiment is a scientific innovation; a way to prevent subtle psychological processes from influencing the results.

What drives this methodological innovation? And what makes the outcome a set of methods we should trust?

In an undergraduate course that I'm teaching this semester, we introduce students to an unconventional definition of science. The course, Letters & Sciences 22: Sense and Sensibility and Science, comes from an interdisciplinary collaboration between a philosopher (John Campbell), a social psychologist (Robert MacCoun), a Nobel-prize-winning physicist (Saul Perlmutter), and a cognitive scientist (me).

On the first day of class, Prof. Perlmutter defines science as a collection of heuristic tricks that are constantly being invented to side-step our mental weaknesses and play to our strengths. On this view, science isn't a recipe, it's a warning. The warning is this: We are fallible.

But recognizing our fallibility, we can do better. Once we learn that placebo effects can occur, we design drug trials to compare drugs against placebos. Once we learn that repeated statistical significance testing can inflate the probability of a false positive, we build in corrective measures. And we shouldn't wait for these lessons to fortuitously come along; we should vigorously seek them out. A common theme in the course, concludes Perlmutter, is that science is about actively hunting for where we are wrong, for where we are fooling ourselves.

Scientific methods thus evolve alongside scientific conclusions, and the engine that drives this change is remarkably simple. In an essay published earlier this month at Edge.org , I argue that science is powerful because it involves the systematic evaluation of alternatives. To determine which evidence is worth pursuing, we consider which alternatives are plausible, and we seek out evidence that will discriminate between them. As we encounter new evidence or new arguments, we evaluate the possibility that alternative conclusions are now better supported, and alternative methods better guides to the truth.

Scientific thinking isn't just a tool for working scientists; it's an approach to getting the facts right by entertaining all the ways we might get the facts wrong. Only when viable alternatives have been eliminated can we be pretty confident we've got something right.

So let me end with a plea. The plea isn't for people to accept any particular scientific consensus, or any particular public fact. It's a plea for people to embrace the value of considering alternative possibilities, and evaluating those possibilities against the best evidence and arguments at our disposal. And it's a plea for us to do so together, with the kinds of evidence we can verify and share, and the kinds of arguments we can subject to public scrutiny. And if you're not convinced, please consider the alternatives.

Tania Lombrozo is a psychology professor at the University of California, Berkeley. She writes about psychology, cognitive science and philosophy, with occasional forays into parenting and veganism. You can keep up with more of what she is thinking on Twitter: @TaniaLombrozo

• scientific knowledge
• public facts
• scientific methods
• alternatives

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

4 The Laws of Scientific Change

Chapter 4: the laws of scientific change.

So far, we have given a number of examples of how both accepted theories and methods of theory evaluation have changed through time. These changes portray scientific mosaics as dynamic and seemingly always in flux. So, you might be wondering: is there some underlying universal mechanism that governs the changes in theories and methods that we see in scientific mosaics? Or is it the case that theories and methods change in a completely random fashion, without exhibiting any general patterns? To answer that question, we have to first tackle the broader philosophical question of whether a general theory of scientific change is possible at all. Doing so will require making a number of clarifications from the outset.

When we consider any particular science, there are two relatively distinct sets of questions that we can ask: particular questions and general questions. Particular questions ask about specific events, data points, phenomena, or objects within a certain science’s domain. Particular questions in a field like ornithology (the study of birds) might be:

Is that blue jay a male or a female?

How many eggs did that snowy owl lay this season?

Did the Canada geese begin flying south late this year?

In contrast, general questions ask about regular patterns, or about classes of events, phenomena, or objects in a certain science’s domain. General questions in ornithology (corresponding with the particular questions above) might be:

What are the physical or morphological differences between male and female blue jays?

What environmental factors affect snowy owl egg production?

What are the typical migration patterns of Canada geese?

Often, answers to particular questions are relatively straightforward theories:

The blue jay in question is a male.

The owl laid a clutch of 3 eggs.

The Canada geese left late.

But interestingly, these questions often depend upon answers to the general questions we asked above. In order to determine whether the blue jay is male or female, for instance, the ornithologist would have to consult a more general theory about typical morphological sex differences in blue jays. The most apt general theories seem to emerge as answers to general questions from within a specific scientific domain.

Now, it is possible – and sometimes necessary – to draw from the general theories of other sciences in order to answer particular questions. For instance, if we did not yet have a general theory for determining the sex of a blue jay, we might rely upon general theories about sexual difference from generic vertebrate biology. That said, while helpful, general theories from related disciplines are often vague and more difficult to apply when one is trying to answer a very specific, particular question.

The question “Can there be a general theory of scientific change?” is therefore asking whether we can, in fact, unearth some general patterns about the elements of science and scientific change – the scientific mosaic, theories, methods, etc. – and whether it’s possible to come up with a general theory about them, on their own terms. We can clearly ask particular questions about different communities, their mosaics, and changes in them:

What different scientific communities existed in Paris ca. 1720?

What version of Aristotelian natural philosophy was accepted in Cambridge around 1600?

What method did General Relativity satisfy to become accepted?

Yet, we can also ask general questions about the process of scientific change, such as:

What is the mechanism by which theories are accepted?

What is the mechanism of method employment?

But are we in a position to answer these general questions and, thus, produce a general theory of scientific change? Or are particular questions about scientific change answerable by general theories from other fields, like anthropology or sociology?

Generalism vs. Particularism

Let’s frame the debate with two opposing conceptions, which we will call generalism and particularism: does the process of changes in theories and methods exhibit certain general patterns? Answering “yes” to this question would make one a supporter of the conception we’ve called generalism . Generalists believe that there is some sort of an underlying mechanism that governs transitions from one theory to the next and one method to the next and, therefore, a general theory of scientific change is possible, in principle. To answer “no” to this question would make one a supporter of what we’ve called particularism . Particularists believe that there can be no general theory of scientific change.

It should be noted at the outset that there is no communal consensus among historians and philosophers of science regarding the issue of particularism vs. generalism. That said, particularism, not generalism , has more widespread acceptance among individual historians and philosophers of science nowadays. They take this position for a number of reasons, four of which we will unpack below.

Argument from bad track record : There have been a number of attempts to fashion theories that could answer general questions about scientific change. Despite the fact that some of these theories were accepted by individual historians and philosophers of science, none have ever become accepted by the community of historians and philosophers of science. While these theories were often ingenious, one of their principal drawbacks has been their failure to account for the most up-to-date knowledge from the history of science. Consider, for instance, Karl Popper’s conception of scientific change, according to which the process of scientific change is essentially a series of bold conjectures and decisive refutations. In this Popperian picture of science, a theory is accepted only provisionally as the best available hypothesis and is immediately rejected once we observe an anomaly, i.e. something that goes against the predictions of the theory. However, historical studies show that scientific communities often exhibit remarkable tolerance towards anomalies. For example, in 1859, it was discovered that the observed orbit of the planet Mercury deviates slightly from the one predicted by the then-accepted Newtonian theory. This was clearly an anomaly for the accepted theory and if science functioned according to Popper’s dicta, the Newtonian theory would have to be rejected. Yet, it is a historical fact that the Newtonian theory remained accepted for another 60 years until it was replaced by general relativity ca. 1920.

These historical failings are explained slightly more thoroughly in points 2 and 3 below. The “argument from bad track record” considers these failed attempts all at once. In short, every general theory of scientific change suggested so far has been left unaccepted. The conclusion of the argument, therefore, is an inductive inference: since every attempt to craft a general theory has ended up unaccepted, it seems likely that no general theory will ever be accepted. This argument isn’t the strongest, since it makes a rather pessimistic inference drawn from very little data, but it is an argument against generalism nonetheless.

Argument from Changeability of Scientific Method : Previous attempts to propose a general theory of scientific change tried to ground those theories in a single set of implicit expectations and requirements which all scientific communities had shared throughout history. That is, philosophers of science like Carnap, Popper, Lakatos and others between the 1920s and the 1970s tried to explicate a single method of science common to all scientific communities, past, present, and future. If we figure out what that method is – they reasoned – we can point to a set of unchanging requirements or expectations to help us answer the general question “how do new scientific theories get accepted?”

But as we know from chapter 3, a more careful historical analysis reveals that there has been no single, transhistorical or transdisciplinary method of science. Scientific methods, in fact, do change through time. If answers to general questions about scientific change depend upon a static method of science, and the method of science is dynamic , then when faced with the question “Can there be a general theory of scientific change?” we would seem compelled to respond “no”.

Argument from Nothing Permanent : One can take this line of reasoning from the changeability of the scientific method a step further. The efforts of historians of science have demonstrated that reconstructing the theories and methods held by any scientific community involves also accounting for an incredibly complex set of social, political, and other factors. For instance, to even begin to reconstruct the theories and methods of the scientific community of the Abbasid caliphate, which existed on the Arabian peninsula between 750–1258 C.E., a historian must be familiar with that particular society’s language, authority structure, and what types of texts would hold the accepted theories of that time. All of these would be starkly different from the factors at play in Medieval Europe during the same period! This is all to say that in addition to showing that there are diverse scientific methods across geography and time, it seems that almost anything can vary between scientific communities. Across time and space, there might be nothing permanent or fixed about scientific communities, their theories, or their methods.

The claim here is that, because neither methods nor nearly anything else can ground patterns of change at the level of the mosaics of scientific communities, there’s no way that we could have a general theory of scientific change, nor general answers to the general questions we might ask about it. Those who marshal this argument therefore eschew any general approach to science and instead focus on answering particular questions about science: questions about the uniqueness and complexity of particular scientific communities or transitions within particular times and places. Often, these historical accounts will be communicated as a focused story or narrative , giving an exposition of a particular historical episode without relying on any general theory of scientific change.

Argument from Social Construction : While historical narratives might be able to avoid invoking or relying upon a general theory of scientific change, they cannot avoid relying upon general theories of some sort. Even the most focused historical narratives invoke general theories in order to make sense of the causes, effects, or relationships within those episodes. It is accepted by historians that Queen Elizabeth I cared deeply for Mary, Queen of Scots. The traditional explanation for this is that they were cousins. Clearly this explanation is based on a tacit assumption, such as “cousins normally care for each other”, or “relatives normally care for each other”, or even “cousins in Elizabethan England cared for each other”, etc. Regardless of what the actual assumption is, we can appreciate that it is a general proposition. Such general propositions are an integral part of any narrative.

For a large swath of contemporary historians, the constitution and dynamics of the scientific mosaic can be explained by referencing the general theories of sociology and anthropology. In other words, the patterns and regularities needed to have such general theories can be found in the way that communities behave, and how individuals relate to those communities. Since there are good arguments for why scientific change lacks these patterns, and since there is no accepted general theory that identifies and explains them, many philosophers and historians of science see the dynamics of scientific theories and methods as social constructs, and thus best explained by the best general theories of sociology and anthropology. So, the argument from “social construction” is slightly more nuanced than the arguments that have come before it. It claims that particular changes in science can be best explained with general theories from sciences like sociology and anthropology, not general theories of scientific change which have typically failed to capture the nuances of history. In short, many philosophers and historians are doubtful about the prospects of a general theory of scientific change.

But what if there were a general theory which offered rebuttals to all four of the previously mentioned arguments for particularism? What if there were a general theory of scientific change which could accommodate the nuances and complexities of historical-geographic contexts, and also managed to recognize and articulate constant features in the historical dynamics of scientific mosaics?

The remainder of this chapter is dedicated to introducing a general theory of scientific change which proposes four laws which can be said to govern the changes in the scientific mosaic of any community. This general theory has formed the centrepiece of a new empirical science called scientonomy , where it is currently accepted as the best available description of the process of scientific change. However, note that from the viewpoint of the larger community of historians, philosophers, and sociologists of science this theory is not currently accepted but merely pursued . In the process of explaining this new general theory of scientific change, we will labour to demonstrate how this general theory rebuts all of the major arguments used to support particularism. Hopefully it will become clear by the end of the chapter why the development and refinement of this theory is worth pursuing, and perhaps even why we might be able to answer “yes” to the question “can there be a general theory of scientific change?”

Method Employment

To begin, let’s do a thought experiment based on the history of medical drug testing. Suppose that you and a group of other scientists want to determine whether a certain medicine can help people with insomnia fall asleep more easily. Let’s call the medicine you’d like to test Falazleep . What you are ultimately doing is seeing whether the theory “Falazleep helps individuals with insomnia fall asleep” is acceptable to the scientific community or not.

One of the most straightforward ways of seeing whether Falazleep can help individuals go to sleep more easily is to simply give the proper dose of Falazleep to people who suffer from insomnia . If some of those people report being able to sleep more easily, then it seems reasonable for the scientific community to accept the theory that Falazleep is therapeutically effective , i.e. that it works, at least in certain cases. What we’ve articulated here is a very basic version of the hypothetico-deductive (HD) method: in order to accept this causal relationship between Falazleep and sleep, you have to confirm your theory’s prediction that, when given to insomniacs, Falazleep will indeed help them sleep.

You and your peers give a group of insomniacs Falazleep and, sure enough, many of them do in fact fall asleep more easily. Having confirmed your theory’s prediction, you and the scientific community accept the theory that Falazleep helps individuals with insomnia fall asleep.

A few years later, you and your peers are asked to test a new drug, Falazleep Z . However, the scientific community has come to the realization that other factors – such as the recipients’ exercise, diet, stress level, and so forth – could have actually been the cause of the reported easier sleep in your previous trial, not Falazleep itself. That is, you and the scientific community now accept the theory of unaccounted effects , which says “factors other than the medication may influence the outcome of a medical drug test study.” Knowing what you know now, can you and your peers test Falazleep Z the same way you tested the original Falazleep? No!

Why not? Because now you and the rest of the scientific community accept that your earlier test – simply giving Falazleep to insomniacs – didn’t even begin to distinguish between the effects of Falazleep and the other variations in the test subjects’ lives. In order for your new test results to be accepted, you have to meet the scientific community’s new expectation: that you find a way to distinguish between the effects of Falazleep Z and other variations in the test subjects’ lives.

So, you and your colleagues take a large group of insomniacs and split them in two groups, each of which you study for the duration of the trial. However, only one group – the so-called “active group” – actually receives Falazleep Z. The other group, which we will label the “control group”, receives no medication. At the end of the trial, if the active group’s improvement with Falazleep Z is significantly greater than the improvement in the control group, then perhaps the scientific community will accept that Falazleep Z is therapeutically effective. This is especially likely because the experimental setup, today called a “controlled trial,” accounts for previously unaccounted effects, such as the body’s natural healing ability, improved weather, diet, and so on.

A few more years go by, and you and your peers are asked to test another drug, Falazleep Zz . But in the meantime, it has been found that medical drug trial participants can sometimes show drastic signs of improvement simply because they believe they are receiving medication, even if they are only being given a fake or substitute (candy, saline solution, sugar pills, etc.). In other words, the scientific community accepted a theory that belief in effective treatment might actually produce beneficial effects, called the placebo effect . Perhaps some of the efficacy of the Falazleep Z trial from a few years back was because some participants in the active group believed they were getting effective medicine…whether the medicine was effective or not!

So, in your trial for Falazleep Zz, you need to now account for this newly discovered placebo effect. To that end, you and your peers decide to give both your active group and your control group pills. However, you only give Falazleep Zz to the active group, while giving the control group sugar pills, i.e. placebos . Importantly, only you (the researchers) know which of the two groups are actually receiving Falazleep Zz. Because both groups are receiving pills, but neither group knows whether they’re receiving the actual medicine, this experimental setup is called a blind trial . Now suppose the improvement in the condition of the patients in the active group is noticeably greater than that of the patients in the control group. As a result, the scientific community accepts that Falazleep Zz is therapeutically efficient.

Another few years pass, and you and your group test the brand-new drug FASTazleep . Since your last trials with Falazleep Zz, the scientific community has discovered that researchers distributing those pills to the control and active groups can give off subtle, unintended cues as to which pills are genuine and which are fake. Unsurprisingly this phenomenon, called experimenter’s bias , prompts test subjects in the active group who have picked up on these cues to improve, regardless of the efficacy of the medication. Because you and your peers accept this theory of experimenter’s bias, you know the scientific community expects a new theory to account for this bias. Consequently, you devise a setup in which neither those administering the pills nor those receiving the pills actually know who is receiving FASTazleep and who is receiving a placebo. In other words, you perform what is nowadays called a double-blind trial .

Hopefully this repetitive example hasn’t put you “FASTazleep”! The repetition was purposeful, it was meant to help elucidate the pattern found not only in the actual history of drug trial testing, but in the employment of new methods in science generally. As you may have noticed, when the scientific community accepted a new theory – say, the existence of the placebo effect – their implicit expectations about what would make a new theory acceptable also changed. Remember, we’ve called these implicit expectations and requirements of a scientific community their method . Thus, we can conclude that changes in our criteria of theory evaluation, i.e. in our methods, were due to changes in our accepted theories:

That new accepted theories cause employed methods to change is the fundamental insight which is the essence of a law of scientific change , which we’ve called the law of method employment. According to the law of method employment, a method becomes employed only when it is deducible from some subset of other employed methods and accepted theories of the time:

This law captures a pattern, a regularity that occurs time and again in the history of science when a community comes to employ a new method. Indeed, it seems to successfully describe the mechanism of both minor changes in employed methods, such as any of the examples from medical drug trial testing above, and even more substantial changes in employed method, such as the shift from the Aristotelian-Medieval (AM) to the hypothetico-deductive (HD) method. Importantly, the third law describes not only that new methods are shaped by new theories, but how those methods are shaped by those theories. As you can see in the formulation of the law, a new method is employed only if that method is a deductive consequence of, i.e. if it logically follows from, some other theories and methods which are part of that scientific community’s mosaic.

Now that we know the mechanism of transitions from one method to the next, we can appreciate why methods are changeable. In chapter 3 we provided a historical argument for the dynamic method thesis by showing how methods of theory evaluation were different in different historical periods. Now we have an additional – theoretical – argument for the dynamic method thesis. On the one hand, we know from the third law that our employed methods are deductive consequences of our accepted theories. On the other hand, as fallibilists, we realize that our empirical theories can change through time. From these two premises, it follows that all methods that assume at least one empirical theory are, in principle, changeable.

This law also helps us cast light on what features of historical mosaics led to the employment of certain methods at certain times. Consider for instance the assumptions underlying the hypothetico-deductive method . It is safe to say that the HD method is a deductive consequence of very specific assumptions about the world.

One such assumption is the idea of complexity , according to which the world, as it appears in observations, is a product of some more fundamental inner mechanism. This assumption has been tacitly or explicitly accepted since the early 18th century. It is because of this assumption that we tolerate hypotheses about the existence of unobservable entities, such as elementary particles, waves, forces, etc. The reason why we tolerate such hypotheses is that we accept the possibility that there might be entities and relations that are not directly observable. In other words, we believe that there is more to the world than meets the eye. We wouldn’t tolerate any hypotheses about unobservables if we believed that the world is composed only of immediately observable entities, properties, relations, etc.

The second assumption underlying the HD method is the idea that any phenomenon can be given many different post hoc explanations which are equally precise. We’ve already witnessed this phenomenon. Recall, for example, different theories attempting to explain free fall, ranging from the Aristotelian “descend towards the centre of the universe” to the general relativistic “inertial motion in a curved space-time”. But since an explanation of a phenomenon can be easily cooked up after the fact, how do we know if it’s any good? This is where the requirement of confirmed novel predictions enters the picture. It is the scientists’ way of ensuring that they don’t end up accepting rubbish explanations, but only those which have managed to predict something hitherto unobserved. A theory’s ability to predict something previously unobserved is valued highly precisely due to the risk of post hoc explanations. Indeed, novel predictions wouldn’t be necessary if there were no risk of cooked up post hoc explanations.

In short, just like the drug testing methods, the HD method too is shaped by our knowledge about the world.

It is important to appreciate that the HD method is also changeable due to the fact that the assumptions on which it is based – those of complexity and post hoc explanations – are themselves changeable.

The same goes for the Aristotelian-medieval method – it was also shaped by the theories accepted at the time. One of the assumptions underlying the AM method is the idea that every natural thing has its nature , an indispensable (substantial) quality that makes a thing what it is. For instance, what makes an acorn an acorn is its capacity to grow into an oak tree. Similarly, the nature of a lion cub is its capacity to grow into a full-fledged lion. Finally, according to this way of thinking, the substantial quality of a human being is the capacity of reason.

The second assumption underlying the AM method is the idea that the nature of a thing can be grasped intuitively by an experienced person. In a nutshell, the idea was that you get to better understand a thing as you gain more experience with it. Consider a beekeeper who has spent a lifetime among bees. It was assumed that this beekeeper is perfectly positioned to inform us on the nature of bees. The same goes for an experienced teacher who, by virtue of having spent years educating students, would know a thing or two about the nature of the human mind.

The requirements of the AM method deductively follow from these assumptions. The Aristotelian-medieval scholars wouldn’t expect intuitive truths grasped by an experienced person if they didn’t believe that the nature of a thing can be grasped intuitively by experienced people or if they questioned the very existence of a thing’s nature.

Once again, we see how a community’s method of theory evaluation is shaped by the theories accepted by that community.

It is important to understand that a method can be composed of criteria of three different types – acceptance criteria , demarcation criteria , and compatibility criteria . So far, when talking about methods, we have focused on acceptance criteria . These are the criteria that determine whether a theory is acceptable or unacceptable. However, there are at least two other types of criteria. Demarcation c riteria determine whether a theory is scientific or unscientific, while compatibility criteria determine whether two theories are compatible or incompatible within a given mosaic.

Since the third law of scientific change applies to all methods, it applies equally to all three types of criteria. While, so far, our historical examples concerned changes in acceptance criteria, the third law also explains how demarcation criteria and compatibility criteria change through time. Below we will discuss some examples of different compatibility criteria and how they are shaped by accepted theories. As for different demarcation criteria, we will discuss them in chapter 6.

To re-emphasize a point made earlier, we claim that this same mechanism of method employment is at work in any historical period, in any scientific community. In science, methods and any criteria they contain are always employed the same way: our knowledge about the world shapes our expectations concerning new theories. But this is not the only transhistorical feature of scientific mosaics and their dynamics. The law of method employment is only one of four laws that together constitute a general theory of scientific change. Let’s look at the remaining three laws in turn.

Theory Acceptance

As we’ve just seen, a mosaic’s method is profoundly shaped by its accepted theories, and methods will often change when new theories are accepted. But how does a scientific community accept those new theories? As with method employment, there seems to be a similarly universal pattern throughout the history of science whenever new theories are accepted into a mosaic.

Whether you’ve noticed it or not we have given you many examples of the mechanism of theory acceptance in action. We saw how Fresnel’s wave theory of light was accepted ca. 1820, how Einstein’s theory of general relativity was accepted ca. 1920, and how other theories like Mayer’s lunar theory and Coulomb’s inverse square law were all accepted by their respective scientific communities. In chapter 3 we also considered the case of Galileo, which was a scenario in which the scientific community did not accept a proposed new theory. So, what was not the case in the Galileo scenario that was the case in every other scenario we have just mentioned? In the case of Galileo, his heliocentric theory did not satisfy the method of the time. Every other theory which became accepted did satisfy the method of their respective communities at the time of assessment.

Indeed, in every situation we’ve considered where a new contender theory is eventually accepted into a mosaic, the theory is only accepted if it somehow meets the requirements of that scientific community’s method. To perhaps oversimplify this finding slightly: a theory is only accepted when a scientific community judges that it is acceptable! One of our earlier versions of the law of theory acceptance was actually very similar to this insight, saying that in order to become accepted into the mosaic, a theory is assessed by the method actually employed at the time. This did the trick for a while, but we came to realize that it only tells us that to be accepted a theory must be assessed , but that it doesn’t tell us the conditions that actually lead to its acceptance . For that reason, we have reformulated the law in a way that, while slightly less elegant, clearly links the theory’s assessment by the method to the possible assessment outcomes:

So, if a theory satisfies a mosaic’s employed method, it is accepted. If a theory fails to satisfy the employed method – if it fails to meet the implicit expectations of that community – it will simply remain unaccepted and will not become part of the mosaic. If it is unclear whether the method has been satisfied or not (“assessment is inconclusive”), the law simply describes the two logically possible outcomes (accepted or not accepted). Most importantly for our purposes, however, are the two clear-cut scenarios: when a theory is assessed by the method of the time, satisfaction of the method always leads to the theory’s acceptance, and failure to satisfy the method always leads to the theory remaining unaccepted.

It is important to remember that there is not one universal scientific method that has been employed throughout history, so the requirements that a theory must satisfy in order to be accepted will depend on the method of that particular community at that particular time. Yes, this means that a theory being assessed by the method of one mosaic might be accepted, and a theory assessed by the method of a different mosaic might remain unaccepted. But methods are not employed willy-nilly, and their requirements are not chosen randomly! Despite the fact that both theories and methods change , these changes seem to follow a stable pattern, which we’ve captured in the two laws we’ve discussed thus far. The exciting diversity and variety in science is not the sign of an underlying anarchism or irrationality, but seems to be the result of stable, predictable laws of scientific change playing out in the rich history of human discovery and creativity.

The laws of method employment and theory acceptance are laws which focus on the way that the elements of mosaics change , and how new elements enter into the mosaic. But while change may be inevitable, we also know that science often displays periods of relative stability. So, is there a standard way in which theories and methods behave when there are no new elements being considered?

Scientific Inertia

When Isaac Newton presented his laws of motion in his 1687 opus Philosophiæ Naturalis Principia Mathematica (Mathematical Principles of Natural Philosophy), his obvious focus was on the dynamics of moving bodies and their influence on one another. But he realized that even objects which were not noticeably interacting with one another were also part of his physical system and should therefore be described as well. Newton’s first law of motion about physical objects therefore answered the question: What happens when something is not being acted upon? His answer was that such an object basically continues doing what it was doing : if it was moving, it continues moving at the same rate in a straight line. If it was at rest, it remains at rest. He called this law his law of inertia – the Latin word inertia means “inactivity.”

What happens when the elements in our mosaic are not being “acted upon”? If there’s no new theory being accepted, or no new method being employed, how do the existing elements of the mosaic behave? It seems that elements in a scientific mosaic maintain their place in that mosaic : that is, if a theory had been accepted, that theory continues to be accepted; if a method had been employed, it continues to be employed. Due to this coincidental similarity between Newton’s first law of motion and the historical behaviour of the elements of scientific mosaics in times of relative stability, we have called this law of scientific change the law of scientific inertia . As precisely formulated, the law of scientific inertia states that an element of the mosaic remains in the mosaic unless replaced by other elements:

There are two major implications of this law that we need to unpack.

First, recall that if a theory is in a mosaic, it’s an accepted theory, and if a method is part of a mosaic, it’s an employed method. That is, if an element is part of the scientific mosaic, it has already been “vetted” by the community. What scientific inertia entails, therefore, is that these same theories are never re-assessed by the method, nor are these methods’ requirements re-deduced . The integration of a particular element into the mosaic happens once (for theories, with the mechanism of theory acceptance; for methods, method employment) and it remains part of the mosaic until it’s replaced by another element.

Second, replacement is essential to understanding this law properly. An element never just “falls out” of a mosaic; it has to be replaced by something. This is most clear in the case of theories, since even the negation of an accepted theory is itself a theory and can only be accepted by satisfying the method of the time. For instance, suppose that a scientific community’s accepted theory changes from “the moon is made of cheese,” to “the moon is not made of cheese.” Is this an example of a theory simply disappearing from the mosaic? No. The negation of a theory is still a theory! Presumably the theory “the moon is not made of cheese” satisfied the method of the time and was accepted as the best available description of the material makeup of the moon, thereby replacing the earlier cheese theory.

What does scientific inertia look like in the real world? In biology today, the accepted theory of evolution is known as the modern synthesis ; it has been accepted since the middle of the twentieth century. Two central sub-theories of the modern synthesis are “evolution results from changes in gene frequencies in populations of organisms,” and “populations adapt to their environments by natural selection.” Within the last 30 years or so, even strong proponents of the modern synthesis have begun to agree that these two sub-theories are inadequate, and that the modern synthesis struggles to account for well-known biological phenomena (such as epigenetics, niche construction, and phenotypic plasticity). Despite this building dissatisfaction, however, the modern synthesis remains the accepted theory in contemporary biology . Why? It is because no contender theory has yet satisfied the method of the biology community, as per the law of theory acceptance. Until a new theory can be proposed that satisfies the method of the biology community – and therefore replace the modern synthesis – the modern synthesis will remain the “best available” theory of evolution.

Compatibility

The fourth and final law of scientific change describes the scientific mosaic at any moment of time and is known as the law of compatibility . The law of compatibility states that at any moment of time, the elements of the scientific mosaic are compatible with each other:

If two or more elements can exist in the same mosaic, we say that they are compatible with one another. If two or more elements cannot exist in the same mosaic, we say those elements are incompatible . This law simply points out that in any one scientific mosaic, all elements – theories and methods – are judged to be compatible.

The notion of compatibility must not be confused with the logical notion of consistency. Two theories are said to be logically consistent when there is no contradiction between them. Compatibility is the ability of theories to coexist within a community’s mosaic regardless of their mutual consistency or inconsistency. Thus, in principle, it is possible for two logically inconsistent theories to be nevertheless compatible within a community’s mosaic. In other words, compatibility is a relative notion: the same pair of theories may be compatible in one mosaic and incompatible in another.

What, ultimately, arbitrates which theories and methods are compatible with one another? In other words, where can we find the implicit requirements (i.e. expectations , criteria ) which the scientific community would have for what is compatible and what is not? You guessed it: the method of the time. What is compatible in a mosaic, therefore, is determined by (and is part of) the method of the time, specifically its compatibility criteria . Just like any other criteria, compatibility criteria can also differ across different communities and time periods. Thus, a pair of theories may be considered compatible in one mosaic but incompatible in another, depending on the compatibility criteria of each mosaic.

But how do these compatibility criteria become employed? They become employed the same way any other method becomes employed: as per the law of method employment, each criterion is a deductive consequence of other accepted theories and employed methods. Let’s look at some examples of different compatibility criteria and how they become employed.

Recall that in the formal sciences we can make claims of absolute knowledge, because any formal proposition is simply a definition or follows from definitions. Communities in formal sciences, therefore, would employ an infallibilist method and consider a theory acceptable only if it is demonstratively true. We also know from the logical law of noncontradiction that contradictory propositions cannot both be true at the same time. Since accepted propositions in formal sciences are considered demonstratively true, it deductively follows that two theories which contradict each other cannot possibly be accepted at the same time in the mosaic of the same formal science community, since only one of them can be demonstratively true. This explains why formal science communities are normally intolerant towards inconsistencies. If we assume that the contemporary formal science community accepts that their theories are absolutely true, it follows deductively that in the mosaic of this formal science community, all logically inconsistent theories are also necessarily incompatible. Thus, this community would be intolerant towards inconsistencies , i.e. for this community inconsistent theories would be incompatible:

This is therefore a main facet of the compatibility criteria of this community’s method. If this scientific community were to accept a new theory that was inconsistent with any earlier theories, those earlier theories would be considered incompatible and therefore rejected from the mosaic.

But are there communities that are tolerant towards inconsistencies? That is, does inconsistency always entail incompatibility ? The short answer is “no”. Let’s consider the mosaic of an empirical science like contemporary physics. The contemporary physics community accepts both quantum mechanics and general relativity as part of their mosaic. Quantum mechanics deals principally with incredibly small objects, like the elements of the standard model (quarks, leptons, and bosons), while general relativity deals with incredibly large-scale objects and high energy interactions, like the bending of space by massive objects and velocities approximating the speed of light. But, interestingly, these two theories are inconsistent with one another when it comes to their interpretation of an object that belongs to both of their domains, such as the singularity within a black hole. It belongs to both domains because singularities are incredibly small and thus should be covered by quantum physics, yet they also have an incredibly large mass and thus should be covered by general relativity. When the two theories are simultaneously applied to singularities, the inconsistency between the two becomes apparent. And yet these two inconsistent theories are simultaneously accepted nowadays and are therefore considered to be compatible despite this inconsistency. Why might that be the case?

Remember that the contemporary physics community (like all empirical science communities) is fallibilist, so it is prepared to accept theories that are far from being demonstratively true. This community is content to accept the best available approximations. (Look back at chapter 2 if you need a refresher as to why!) In addition, this empirical science community also accepts that contradictory propositions can both be approximately (or quasi- ) true. If theories cannot be absolutely true, then it is at least possible that two obviously inconsistent theories might still nevertheless be considered – in certain circumstances – compatible. In this case, physicists realize that they have two incredibly successful theories that have independently satisfied the employed HD method, but that these two theories focus on different scales of the physical universe: the tiny, and the massive, respectively. Their fallibilism and the two distinct domains carved out by each branch of physics lead us to conclude that physicists are tolerant towards inconsistencies :

Naturally, an empirical science community need not necessarily be tolerant towards all inconsistencies. For instance, it may limit its inconsistency toleration only to accepting inconsistent theories that belong to two sufficiently different domains. For example, an empirical science community can be tolerant towards inconsistencies between two accepted theories when each of these theory deals with a very distinct scale. Alternatively, it may only be tolerant towards inconsistencies between an accepted theory and some of the available data obtained in experiments and observations. In any event, it shouldn’t be tolerant towards all possible inconsistencies to qualify as inconsistency-tolerant.

The Four Laws of Scientific Change

We have now introduced you to all four laws of scientific change. But we didn’t introduce you to them in numerical order! The typical ordering of the laws is this:

Scientific inertia was given the honour of being the first law , as something of an homage to Newton’s laws of motion (as explained above). The law of compatibility has been called the zeroth law precisely because it is a “static” law, describing the state of the mosaic during and between transitions. For the purposes of this textbook, it is important to learn the numbers of each respective law, since the numbers will act as a helpful shorthand moving forward. For instance, we can simply reference “the third law” instead of writing about “the law of method employment”.

These four laws of scientific change together constitute a general theory of scientific change that we will use to help explain and understand episodes from the history of science, as well as certain philosophical topics such as scientific progress (chapter 5) and the so-called demarcation problem (chapter 6). The laws serve as axioms in an axiomatic-deductive system, and considering certain laws together helps us shed even more light on the process of scientific change than if we only consider the laws on their own. In an axiomatic-deductive system, propositions deduced from axioms are called theorems . Currently, over twenty theorems have been deduced from the laws of scientific change, but we will only briefly consider three.

Some Theorems

We’ve talked about how theories are accepted, but can we be more explicit about how they are rejected? If we look at the first and zeroth laws together we can derive the theory rejection theorem . By the first law, an accepted theory will remain accepted until it is replaced by other theories. By the zeroth law, the elements of the scientific mosaic are always compatible with one another. Thus, if a newly accepted theory is incompatible with any previously accepted theories, those older theories leave the mosaic and become rejected , and are replaced by the new theory. This way compatibility is maintained (according to the zeroth law) and the previously accepted theories are replaced by the new theory (according to the first law).

How do new theories become appraised? Specifically, is it at all possible to evaluate a theory in isolation from other theories? For a long time, it was believed by philosophers that the task of theory evaluation is to take the theory and determine whether it is absolutely true or false. If a theory passes the evaluation, then it is considered absolutely true; if not, it is considered false. This is the gist of the conception of absolute theory appraisal , according to which it is possible to assess a standalone theory without comparing it to any other theory. However, this conception of absolute appraisal would only make sense if we were to believe that there is such a thing as absolute certainty. What happens to the notion of theory appraisal once we appreciate that all empirical theories are fallible? When philosophers realized that no empirical theory can be shown to be absolutely true, they came to appreciate that the best we can do is to compare competing theories with the goal of establishing which among competing theories is better . Thus, they arrived at what can be called the conception of comparative theory appraisal . On this view of theory appraisal, any instance of theory evaluation is an attempt to compare a theory with its available competitors, and therefore, theory appraisal is never absolute. In other words, according to comparative theory appraisal, there is no such thing as an appraisal of an individual theory independent of other theories.

In recent decades, it has gradually transpired that while all theory appraisal is comparative, it is also necessarily contextual , as it depends crucially on the content of the mosaic in which the appraisal takes place. Specifically, it is accepted by many philosophers and historians these days that the outcome of a theory’s appraisal will depend on the specific method employed by the community making the appraisal. Thus, it is conceivable that when comparing two competing theories, one community may prefer one theory while another may prefer the other, depending on their respective employed methods. We can call this view contextual theory appraisal . Importantly, it is this view of theory appraisal that follows from the laws of scientific change.

Considering the first and second laws, we can derive the contextual appraisal theorem . By the first law, a theory already in the mosaic stays in the mosaic insofar as it is not replaced by other theories. This means that once a theory is accepted into the mosaic, it is no longer expected to satisfy any requirements to stay in the mosaic. By the second law, a theory is assessed by the method employed at the time of the assessment. In other words, the assessment takes place when members of a community attempt to bring a theory into the mosaic. As such, theory assessment is an assessment of a proposed modification of the mosaic by the method employed at the time. In other words, theory assessment is contextual as it takes place within a particular historical/geographical context, by that particular scientific community’s method .

What happens when two incompatible theories satisfy the community’s employed method at the same time? By the second law, we know that if a theory satisfies the method of the time, it will be accepted. From the zeroth law, however, we know that all elements in a mosaic are compatible with one another. So, if two incompatible theories simultaneously satisfy the same employed method, to maintain compatibility the mosaic itself splits : you would have one entirely compatible mosaic containing one theory as accepted, and a second entirely compatible mosaic containing the other. This is known as the mosaic split theorem .

There are many examples of mosaic split in the history of science, the most famous of which occurred between the Newtonian and Cartesian mosaics at the turn of the 18th century. Up until this point the scientific community throughout Europe employed the Aristotelian-Medieval method. You’ll recall that the acceptance criteria for the AM method required that the theory be intuitive to an experienced person. Isaac Newton’s vast axiomatic-deductive system of physical theorems was derived from his three simple laws of motion and his law of universal gravitation. René Descartes’ equally impressive axiomatic-deductive system (incompatible with that of Newton) was similarly deduced from a set of intuitively obvious axioms, such as “Matter is extension.” Faced with these two impressively comprehensive sets of theories, the scientific community found that both satisfied their (vague) AM acceptance criterion of being “intuitive”: both systems began from easily-graspable axioms and validly deduced a plethora of theorems concerning different aspects of the physical world from them. The result was that both theories became accepted, but that the mosaic itself split in two: one Newtonian, one Cartesian.

But what historical factors decide who becomes Newtonian and who becomes Cartesian? The mosaic split theorem tells us that, given the circumstances, a split is going to take place. Yet, it doesn’t say which part of a previously unified community is going to accept Newtonianism and which part is going to accept Cartesianism. From the perspective of the laws of scientific change, there is nothing determining which of the two new communities will accept what. It’s reasonable to suspect that a variety of sociocultural factors play a role in this process. Some sociological explanations point to the fact that France, home to Descartes, and Britain, home to Newton, were rival nations celebrating and endorsing the discoveries and advancements of their own people first. Other explanations pointing to the theological differences between the mosaics of France and Britain from before 1700, with Catholicism accepted in the former and Anglicanism in the latter. Keep in mind that in early modern Europe, theological propositions and natural philosophical propositions were typically part of the same mosaic and had to be compatible with one another. In such a case, the acceptance of Cartesianism and Newtonianism in France and the Continent respectively represented the widening rather than beginning of a mosaic split. We will have an opportunity to look at these two mosaics (and explore exactly why they were incompatible) in the history chapters of this textbook.

Can there be a general theory of scientific change? To begin answering the question we first made a distinction between general and particular questions, and between general and particular theories. We went on to clarify that generalists answer “yes” to this question, and that particularists answer “no.” Afterwards we introduced four arguments used to support particularism. However, we went on to argue for generalism, and presented the four laws of scientific change as a promising candidate for a general theory of scientific change. By way of review, let’s see how we might respond to the four main arguments for particularism via the laws of scientific change.

Bad Track Record : While poor past performance of general theories of scientific change is indeed an argument against generalism, it doesn’t rule out the possibility of the creation of a new theory that has learned from past mistakes. The laws of scientific change take seriously history, context, and change, and seem to succeed where other theories have failed. It’s a contender, at least!

Nothing Permanent (Including the Scientific Method) : Combining the second and third arguments, particularists note that stability and patterns are the cornerstone for answering general questions with general theories, and “science” doesn’t seem to have them. With no fixed method and with highly unique historical contexts, the prospect for grounding a general theory seems nearly impossible. The laws of scientific change, however, are not grounded in some transhistorical element of a mosaic. Rather, they recognize that the way science changes , the patterns of scientific change, are what remain permanent throughout history. The four laws are descriptions of the stable process by which theories and methods change through time. Besides this, the laws, in their current form, don’t ignore the uniqueness and peculiarities of certain historical periods. The laws rather incorporate them into science’s transhistorical dynamic (see the contextual appraisal & mosaic split theorems ).

Social Construction: In the absence of a general theory of scientific change, some historians have attempted to explain individual scientific changes by means of the theories of sociology and anthropology. But hopefully our exposition of the laws has, at least, helped to demonstrate that the process of scientific change has its own unique patterns that are worth studying and describing on their own terms. Indeed, the laws show that not only can general questions be asked about science, but general answers seem to be discoverable as well. Using general theories from a related discipline can be helpful, but why stick with more vague theories that are harder to apply when more specific theories are at your fingertips?

While particularism remains the currently accepted position in this debate, it is clearly unjustified, as all of its main arguments are flawed:

We will pursue and use the laws of scientific change throughout the remainder of this textbook. As fallibilists, we realize that the laws of scientific change are imperfect and will likely be improved. But we are confident that the laws of scientific change as they stand now are at least a valuable tool for parsing the sometimes overwhelmingly complex history of science.

Introduction to History and Philosophy of Science Copyright © by Barseghyan, Hakob; Overgaard, Nicholas; and Rupik, Gregory is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Share This Book

9 Great Ways to Teach Variables in Science Experiments

by Katrina | Feb 17, 2024 | Pedagogy , Science | 1 comment

Science is a journey of exploration and discovery, and at the heart of every scientific experiment lies the concept of variables. Variables in science experiments are the building blocks of experimentation, allowing scientists to manipulate and measure different elements to draw meaningful conclusions.

Teaching students about variables is crucial for developing their scientific inquiry skills and fostering a deeper understanding of the scientific method.

In this blog post, we’ll explore the importance of teaching variables in science experiments, delve into the distinctions between independent, dependent, and controlled variables, and provide creative ideas on how to effectively teach these variable types.

So grab a coffee, find a comfy seat, and relax while we explore fun ways to teach variables in science experiments! 

The Importance of Teaching Variables in Science Experiments:

Foundation of Scientific Inquiry: Variables form the bedrock of the scientific method. Teaching students about variables helps them grasp the fundamental principles of scientific inquiry, enabling them to formulate hypotheses, design experiments, and draw valid conclusions.

Critical Thinking Skills: Understanding variables cultivates critical thinking skills in students. It encourages them to analyze the relationships between different factors, question assumptions, and think systematically when designing and conducting experiments.

Real-world Application: Variables are not confined to the laboratory; they exist in everyday life. Teaching students about variables equips them with the skills to critically assess and interpret the multitude of factors influencing phenomena in the real world, fostering a scientific mindset beyond the classroom.

In addition to the above, understanding scientific variables is crucial for designing an experiment and collecting valid results because variables are the building blocks of the scientific method.

A well-designed experiment involves the careful manipulation and measurement of variables to test hypotheses and draw meaningful conclusions about the relationships between different factors. Here are several reasons why a clear understanding of scientific variables is essential for the experimental process:

1. Precision and Accuracy: By identifying and defining variables, researchers can design experiments with precision and accuracy. This clarity helps ensure that the measurements and observations made during the experiment are relevant to the research question, reducing the likelihood of errors or misinterpretations.

2. Hypothesis Testing: Variables in science experiments are central to hypothesis formulation and testing. A hypothesis typically involves predicting the relationship between an independent variable (the one manipulated) and a dependent variable (the one measured). Understanding these variables is essential for constructing a hypothesis that can be tested through experimentation.

3. Controlled Experiments: Variables, especially controlled variables, enable researchers to conduct controlled experiments. By keeping certain factors constant (controlled variables) while manipulating others (independent variable), scientists can isolate the impact of the independent variable on the dependent variable. This control is essential for drawing valid conclusions about cause-and-effect relationships.

4. Reproducibility: Clear identification and understanding of variables enhance the reproducibility of experiments. When other researchers attempt to replicate an experiment, a detailed understanding of the variables involved ensures that they can accurately reproduce the conditions and obtain similar results.

5. Data Interpretation: Knowing the variables in science experiments allows for a more accurate interpretation of the collected data. Researchers can attribute changes in the dependent variable to the manipulation of the independent variable and rule out alternative explanations. This is crucial for drawing reliable conclusions from the experimental results.

6. Elimination of Confounding Factors: Without a proper understanding of variables, experiments are susceptible to confounding factors—unintended variables that may influence the results. Through careful consideration of all relevant variables, researchers can minimize the impact of confounding factors and increase the internal validity of their experiments.

7. Optimization of Experimental Design: Understanding variables in science experiments helps researchers optimize the design of their experiments. They can choose the most relevant and influential variables to manipulate and measure, ensuring that the experiment is focused on addressing the specific research question.

8. Applicability to Real-world Situations: A thorough understanding of variables enhances the applicability of experimental results to real-world situations. It allows researchers to draw connections between laboratory findings and broader phenomena, contributing to the advancement of scientific knowledge and its practical applications.

The Different Types of Variables in Science Experiments:

There are 3 main types of variables in science experiments; independent, dependent, and controlled variables.

1. Independent Variable:

The independent variable is the factor that is deliberately manipulated or changed in an experiment. The independent variable affects the dependent variable (the one being measured).

Example : In a plant growth experiment, the amount of sunlight the plants receive can be the independent variable. Researchers might expose one group of plants to more sunlight than another group.

2. Dependent Variable:

The dependent variable is the outcome or response that is measured in an experiment. It depends on the changes made to the independent variable.

Example : In the same plant growth experiment, the height of the plants would be the dependent variable. This is what researchers would measure to determine the effect of sunlight on plant growth.

3. Controlled Variable:

Controlled variables, also called constant variables, are the factors in an experiment that are kept constant to ensure that any observed changes in the dependent variable are a result of the manipulation of the independent variable. These are not to be confused with control groups.

In a scientific experiment in chemistry, a control group is a crucial element that serves as a baseline for comparison. The control group is designed to remain unchanged or unaffected by the independent variable, which is the variable being manipulated in the experiment.

The purpose of including a control group is to provide a reference point against which the experimental results can be compared, helping scientists determine whether the observed effects are a result of the independent variable or other external factors.

Example : In the plant growth experiment, factors like soil type, amount of water, type of plant and temperature would be control variables. Keeping these constant ensures that any differences in plant height can be attributed to changes in sunlight.

Science variables in science experiments

Want a FREE digital escape room?

Enter your email address here and I'll send you a FREE no-prep digital escape room on the topic 'Scientific method & science equipment'.

You have successfully joined our subscriber list.

We will not send you spam. 

You can unsubscribe at any time.

Best resources for reviewing variables in science experiments:

If you’re short on time and would rather buy your resources, then I’ve compiled a list of my favorite resources for teaching and reviewing variables in science experiments below. While there is nothing better than actually doing science experiments, this isn’t feasible every lesson and these resources are great for consolidation of learning:

1. FREE Science Variables Posters : These are perfect as a visual aide in your classroom while also providing lab decorations! Print in A4 or A3 size to make an impact.

2. Variable scenarios worksheet printable : Get your students thinking about variable with these train your pet dragon themed scenarios. Students identify the independent variable, dependent variable and controlled variables in each scenario.

3. Variable Valentines scenarios worksheet printable : Get your students thinking about variables with these cupid Valentine’s Day scenarios. Students identify the independent variable, dependent variable and controlled variables in each scenario.

4. Variable Halloween scenarios worksheet printable : Spook your students with these Halloween themed scenarios. Students identify the independent variable, dependent variable and controlled variables in each scenario.

5. Scientific Method Digital Escape Room : Review all parts of the scientific method with this fun (zero prep) digital escape room! 

6. Scientific Method Stations Printable or Sub Lesson : The worst part of being a teacher? Having to still work when you are sick! This science sub lesson plan includes a fully editable lesson plan designed for a substitute teacher to take, including differentiated student worksheets and full teacher answers. This lesson involves learning about all parts of the scientific method, including variables.

Digital Lab Equipment Escape Room Middle School Science

Scientific Method Digital Escape Room Science Middle School

Science Graphing Skills Escape Room Middle School

9 teaching strategies for variables in science experiments.

To help engage students in learning about the different types of scientific variables, it is important to include a range of activities and teaching strategies. Here are some suggestions:

1. Hands-on Experiments: Conducting hands-on experiments is one of the most effective ways to teach students about variables. Provide students with the opportunity to design and conduct their experiments, manipulating and measuring variables to observe outcomes.

Easy science experiments you could include might relate to student heart rate (e.g. before and after exercise), type of ball vs height it bounces, amount of sunlight on the growth of a plant, the strength of an electromagnet (copper wire around a nail) vs the number of coils.

Change things up by sometimes having students identify the independent variable, dependent variable and controlled variables before the experiment, or sometimes afterwards.

Consolidate by graphing results and reinforcing that the independent variable goes alone the x-axis while the dependent variable goes on the y-axis.

2. Teacher Demonstrations:

Use demonstrations to illustrate the concepts of independent, dependent, and controlled variables. For instance, use a simple chemical reaction where the amount of reactant (independent variable) influences the amount of product formed (dependent variable), with temperature and pressure controlled.

3. Case Studies:

Introduce case studies that highlight real-world applications of variables in science experiments. Discuss famous experiments or breakthroughs in science where variables played a crucial role. This approach helps students connect theoretical knowledge to practical situations.

4. Imaginary Situations:

Spark student curiosity and test their understanding of the concept of variables in science experiments by providing imaginary situations or contexts for students to apply their knowledge. Some of my favorites to use are this train your pet dragon and Halloween themed variables in science worksheets.

5. Variable Sorting Activities:

Engage students with sorting activities where they categorize different variables in science experiments into independent, dependent, and controlled variables. This hands-on approach encourages active learning and reinforces their understanding of variable types.

6. Visual Aids:

Utilize visual aids such as charts, graphs, and diagrams to visually represent the relationships between variables. Visualizations can make abstract concepts more tangible and aid in the comprehension of complex ideas.

7. Technology Integration:

Leverage technology to enhance variable teaching. Virtual simulations and interactive apps can provide a dynamic platform for students to manipulate variables in a controlled environment, fostering a deeper understanding of the cause-and-effect relationships.

Websites such as   Phet   are a great tool to use to model these types of scientific experiments and to identify and manipulate the different variables

8. Group Discussions:

Encourage group discussions where students can share their insights and experiences related to variables in science experiments. This collaborative approach promotes peer learning and allows students to learn from each other’s perspectives.

9. Digital Escape Rooms:

Reinforce learning by using a fun interactive activity like this scientific method digital escape room.

Body Systems Worksheets, Stations and Activities Bundle

The Musculoskeletal System Worksheet Stations

Engaging Cardiovascular System Worksheet Stations

Teaching variables in science experiments is an essential component of science education, laying the groundwork for critical thinking, inquiry skills, and a lifelong appreciation for the scientific method.

By emphasizing the distinctions between independent, dependent, and controlled variables and employing creative teaching strategies, educators can inspire students to become curious, analytical, and scientifically literate individuals. 

What are your favorite ways to engage students in learning about the different types of variables in science experiments? Comment below!

Note: Always consult your school’s specific safety guidelines and policies, and seek guidance from experienced colleagues or administrators when in doubt about safety protocols. 

Teaching variables in science experiments

About the Author

Katrina Harte is a multi-award winning educator from Sydney, Australia who specialises in creating resources that support teachers and engage students.

Station activities for middle school

Great web site you’ve got here.. It’s difficult to find excellent writing like yours nowadays. I truly appreciate individuals like you! Take care!!

Recent Posts

Check out my best selling no-prep lesson activities!

• Skip to primary navigation
• Skip to main content
• Skip to primary sidebar
• MEMBER LOGIN

Hands On As We Grow®

Hands on kids activities for hands on moms. Focusing on kids activities perfect for toddlers and preschoolers.

50 Amazingly Simple Science Experiments for Kids at Home

Science Kindergartners Preschoolers Experiment Resources 30 Comments

Kids love experimenting , and these 50 simple science experiments for kids at home from Brigitte are perfect for all ages! Plus, you probably already have the basic supplies at home.

My daughters and I have had a lot of fun doing science experiments. Each year when we create our spring and summer list , we make sure to include “science days” which are days filled with science experiments.

Sometimes our science experiments don’t work according to plan, but I have been told that all scientists have failures with experiments from time to time.

It’s okay if they aren’t all successes.

Get the FREE Science Experiments Download

50 Simple Science Experiments with Supplies You Already Have

I love these 50 simple science experiments for you to try with your little scientists. They all use basic household supplies that you probably already have at home!

Most of these are experiments my daughters and I have done together. I hope you enjoy them as much as we have!

Get little ones involved with these easy toddler-friendly science experiment ideas!

Simple Science Experiments with Water

Not only can water  be a blast to play in, but water plus a few basic supplies equals a lot of science fun!

• Make an orange sink and float with an orange buoyancy experiment from Playdough to Plato.
• Compare the amount of salt in different types of water with this salty egg experiment  as seen on Uplifting Mayhem.
• Do a little more sinking or floating with a fun sink or float experiment  even toddlers can do from Hands On As We Grow.
• Use the free printable to record what sinks or floats in an outdoor experiment from Buggy and Buddy.
• Create some beautiful pieces of paper with this rainbow paper experiment from Science Kiddo.
• Talk about solutions as you try the “what dissolves in water” experiment  as seen on Hands On As We Grow.
• Learn about water absorption with this simple experiment from Little Bins for Little Hands.
• Mix some fun colors with this oil and water experiment  from Fun Learning for Kids.
• Make your own lava lamp , just like on  Hands On As We Grow.
• Can you keep all the water in the bag? Try it with a  leak-proof bag experiment  as seen on Hands On As We Grow.
• Learn about surface tension with this  magic finger pepper experiment  found on Hands On As We Grow.
• Make your own  water cycle in a bottle  as seen on A Dab of Glue Will Do.

Simple Science Experiments with Baking Soda and Vinegar

Baking soda + vinegar = a great chemical reaction! This fizzy reaction can fuel a variety of simple science experiments at home.

First of all, we have tested and found out the absolute best combination of baking soda and vinegar to get the best reaction possible. It makes a difference if you add vinegar to baking soda or vice versa! And how much you use!

• Inflate a balloon without blowing into it with a baking soda and vinegar balloon experiment  as seen on Little Bins for Little Hands.
• Practice colors as you do a baking soda and vinegar with color experiment as seen on Hands On As We Grow.
• Have fun outside with an outdoor volcano eruption  as seen on Preschool Inspirations.
• Have more volcano fun by making apple volcanoes as seen on The Resourceful Mama.
• Learn about acids and bases and the chemical reaction that occurs when you make apple seeds dance with a   jumping apple seeds experiment  as seen on JDaniel4s Mom.
• Watch some rice dance with a   dancing rice experiment as seen on Green Kid Crafts.
• Continue your dance party by making raisins dance with a dancing raisin experiment  as seen on 123 Homeschool 4 Me. What other items can you get to dance?
• Learn more about acids and bases by dissolving a sea shell as seen on Teach Beside Me.
• Make an egg shell disappear with this disappearing egg activity  as seen on Premeditated Leftovers.
• See how far you can launch a soda bottle with this baking soda powered boat as seen on Science Sparks.
• Make your own rocks (or eggs) with this fizzy treasure rocks experiment as seen on Living Life and Learning.
• Have some fun this summer with this frozen vinegar experiment as seen on Inspiration Laboratories.

Plant Themed Simple Science Experiments

Enjoy learning about seeds, plant parts, and how plants grow with these simple science experiments.

• Learn about how plants soak up water through their stems with a flower experiment for kids  from Growing A Jeweled Rose.
• Watch seeds sprout as you grow seeds in a jar  as seen on Teaching Mama.
• Learn about the parts of the seed with a seed coat experiment as seen on Gift of Curiosity.
• Build a house out of sponges and then watch it sprout with this sprout house as seen on The Stem Laboratory.
• Learn what liquids allow seeds to grow the best with this seed experiment  as seen on Gift of Curiosity.
• Explore how plants grow towards the light with this shoe-box maze experiment from Plants for Kids.

Animal Themed Simple Science Experiments

Learning about animals can be even more fun with some simple hands-on simple science experiments.

• Find out more about giraffes and create some giraffe spots  as seen on Preschool Powol Packets.
• Learn about how animals in the Arctic keep warm by making an arctic glove  as seen on Steve Spangler Science.
• Discover how penguins stay dry with a penguin feather experiment as seen on Raising Little Superheroes.
• Learn about different bird beaks with a bird beak experiment as seen on Blessed Beyond a Doubt.
• Explore how fish (and hermit crabs) breathe with this gill experiment  as seen on Preschool Powol Packets.
• Learn about sharks with a   shark buoyancy experiment as seen on Little Bins for Little Hands.

Even More Simple Science Experiment for Kids at Home!

If you are still looking for more science fun, you may enjoy the following simple science experiments.

• Find out how sugary drinks hurt teeth with an  eggs-periment  as seen on Feels Like Home Blog.
• Discover geodes (the state rock of Iowa) with this eggshell geode crystal experiment  as seen on Science Bob.
• Learn about air pressure with an egg and bottle experiment  as seen on Science Sparks.
• Find out what causes an apple to brown with this apple science experiment  as seen on Teach Beside Me.
• Make an  edible bubble apple with an experiment as seen on Preschool Powol Packet.
• Learn more about surface tension with a penny and water experiment  as seen on Artful Parent.
• Mix colors like magic with this color changing milk experiment  from Hands On As We Grow.
• Blow up a balloon with this soda and balloon experiment from Learn Play Imagine.
• Practice letters by making beautiful crystal letters as seen on Books and Giggles.
• Make your own indoor hovercraft  as seen on Living Life and Learning.
• Learn about colors with this beautiful butterfly chromatography craft  as seen on Buggy and Buddy.
• Make soap souffle  as seen on Steve Spangler Science.
• After talking about liquids and solids (and finding them in your own home), create oobleck  as seen on Babble Dabble Do. Is it a liquid, or is it a solid?
• Learn about frost by making some indoor frost as seen on Little Bin for Little Hands.
• Make your own homemade butter in a jar as seen on Happy Hooligans.

What scientific experiment will you try first?

About Brigitte Brulz

Brigitte Brulz is a homeschooling mom of two daughters, wife of her high school sweetheart, and author of Jobs of a Preschooler and Pickles, Pickles, I Like Pickles. She offers free coloring pages and activity ideas on her website at BrigitteBrulz.com .

More Hands on Kids Activities to Try

Reader Interactions

30 comments.

college brawl says

March 13, 2024 at 1:05 am

Wow, these experiments look like so much fun! I can’t wait to try them out with my kids. We’re always looking for new and creative ways to learn about science at home, and these experiments look like they’ll be perfect for us. Thanks for sharing! 😊

threadsBay says

August 31, 2023 at 3:13 am

I love science experiments! This one is really simple and easy to do.

Leave a Comment Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed .

What Parents Have to Say…

Shop ebooks of activities.

Get activity plans delivered to your inbox, every week!

Activities that hands-on parents absolutely love.

50+ Simple Activities for Toddlers

40+ Awesome Number Activities for Preschoolers

6 Different Activities for 6 Lines of Tape

Why Routines for Kids are Important

What Toddler Crafts & Art Projects Can We Do? 30 Ideas

Top Physical Activities for Toddlers! Mom, Embrace the Energy!

Get started having fun with your kids.

PLAN THE FUN WITH THE FREE KIDS ACTIVITIES PLANNER! AND RECEIVE ACTIVITIES EVERY WEEK!

• Preschoolers
• Kindergartners
• Grade School
• Literacy & ABCs
• Math & 123s
• Art Projects
• Gross Motor
• Shop Activity Plans
• Member Login