Why repeat experiments

Reliability is about the consistency of a measure, and validity is about the accuracy of a measure. 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. Bad experiments move metrics by confusing or tricking your users.

They make things harder for your users, rather than solving underlying problems. Good experiments are conceived as bets. You know they have a chance to fail, but based on the info you have available, it is a good investment to make. These changing quantities are called variables. A variable is any factor, trait, or condition that can exist in differing amounts or types. An experiment usually has three kinds of variables: independent, dependent, and controlled.

In a study with mice, you could control for all of these factors—you could have mice with identical genetics, all of the same age and gender, that are fed the same diet, that weigh the same amount, and perform the same exercise regime. But it would be impossible to do a similar fully controlled study with humans. And each factor you try to control, the fewer people would be available to your study and the more difficult it would be to recruit subjects.

An alternative is to limit only some of the variables, and measure the remaining covariates in order to factor them into your final data-analysis model.

Using the model, you can mathematically subtract out the effects of the covariates and still see the effects of the variable in which you're interested: the cholesterol-lowering drug. These resources provide additional information about how to design experiments and increase the signal-to-noise ratio in scientific data:. Menu Science Projects. Project Guides. View Site Map. Science Projects. Grade Levels. Physical Science. Earth and Environmental Science.

Behavioral and Social Science. Increasing the Ability of an Experiment to Measure an Effect. Quantitative Variables Technique for increasing the signal-to-noise ratio What is it? When is it helpful? Examples of when to use it Making repeated measurements Measuring a single item or event more than once to eliminate error in measuring.

More measurements of a single event lead to greater confidence in calculating an accurate average measurement. Especially helpful if an individual measurement may have a lot of variability; because it has to be made quickly, it is hard to determine the exact endpoint, or is technically difficult and thus prone to errors.

Does not add value if the measurement is clear-cut, like the answer to a survey question about a person's age or measuring the dimensions of a room in meters.

How many drops of acid does it take to change the color of this indicator solution? Run the reaction several times on aliquots of the same solution. Test the same exact graphics card multiple times. How long does this turtle spend underwater before surfacing for a breath? Observe the same turtle multiple times. Increasing the sample size Increasing the number of items, or people, that you are collecting data from increases the probability that what you are observing is indicative of the whole population.

Calculations can be made to determine how large the sample size needs to be. See the guide on determining the best sample size for a survey for more details. Especially helpful when you are trying to draw conclusions about an entire population. Does not apply if your conclusions are intended to be specific to an individual or single item. Do teenagers eat healthy foods? Survey a large number of teens, not just five people who always hang out together, about their daily diets.

How do the lung capacities of smokers versus non-smokers compare? Take measurements from many smokers and non-smokers. How long does a 9-volt V battery from brand X power a flashlight? Test multiple manufacturing batches of brand X's 9-V battery. How many replicates do you need to be a statistically sound experiment? What is the minimum sample size for Anova?

What is positive control in experiment? Do control groups need replicates? Can you do an experiment without a control group? What is the difference between control group and constant? Why is a control group necessary? What is control group example? Which person is the control group? How do you choose a control group? What makes a good control group? What is a control group simple definition?

How do you identify a control in an experiment? What is the purpose of a control in an experiment? What is the purpose of a control group in biology? What is an example of a control in an experiment? Why is it important for a biologist to include a control group in an experiment?

What is the constant in an experiment? What is constant give example? What factors should be kept constant in an experiment? Science is not about proving that "I am right! It is usually a group effort, with each scientist adding her own perspective to the idea, giving us a better understanding and often raising new questions to explore.

Skip to main content. State Science Standards Help! Privacy Policy. If your system blocks Vimeo, click here to use the alternate player In studying the processes of science, you will often run into two words, which seem similar: Repetition and Replication. Please log in.