Why do repeat experiments

Sometimes it is a matter of random chance, as in the case of flipping a coin. Just because it comes up heads the first time does not mean that it will always come up heads. By repeating the experiment over and over, we can see if our result really supports our hypothesis What is a Hypothesis?

Sometimes the result might be due to some variable that you have not recognized. In our example of flipping a coin, the individual's technique for flipping the coin might influence the results. To take that into consideration, we repeat the experiment over and over with different people, looking closely for any results that don't fit into the idea we are testing. Results that don't fit are important! Figuring out why they do not fit our hypothesis can give us an opportunity to learn new things, and get a better understanding of the idea we are testing.

Once we have repeated our testing over and over, and think we understand the results, then it is time for replication. That means getting other scientists to perform the same tests, to see whether they get the same results.

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. Randomization of samples Using a lottery system to assign samples to different experimental and control groups within a given experiment helps make the starting makeup of the groups as equal as possible, even for variables you might have overlooked. Some experiments can be completely randomized; other involve blocking first.

Blocking allows for the creation of homogenous groups, like males versus females, and then randomization within the block. This variation is done when the researcher suspects that there may be scientifically important differences between experimental subjects.

Especially critical when the population you are drawing your samples from which is the population you want to make conclusions about is very heterogeneous. May not apply if you need to stratify your population because you want to be able to draw different conclusions about each sub-group. For example, men vs. Which fertilization technique increases crop yield the most? Assign fertilizer treatment to each plot of land by lottery, thus evening out effects of other variables, like soil makeup and water content, among the experimental groups.

Does this medication decrease osteoporosis? Randomly assign people to determining whether a medication is effective. Randomly assign patients to placebo or medication group. Randomization of experiments Using a lottery system to determine the order of carrying out related experiments, rather than relying on an apparently logical order that may introduce other overlooked variables.

Especially critical when you have related experiments from which you are going to draw a single meta conclusion. Applies to both related experiments done serially using the same equipment, and related experiments done in parallel on different equipment. Does the length of time plastic is pressed in a mold affect the final strength of the plastic? Rather than running experiments testing 10, 20, 30, etc. The randomization eliminates potential effects from other variables like different amounts of mixing time for the plastic as the experiments progress and changes to the temperature of the mold over the course of all the experiments.

Does the color of a mouse maze walls affect the total time it takes for mice to find their way through? You can test reliability through repetition. The more similar repeated measurements are, the more reliable the results. However, the entire result of the experiment can be improved through repetition and analysis, as this may reduce the effect of random errors.

Getting the same result when an experiment is repeated is called replication. If research results can be replicated, it means they are more likely to be correct. They indicate how well a method, technique or test measures something. 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.