The number of participants needed for an experiment depends on several factors, including the study's design, the expected effect size, and the desired statistical power (commonly set at 0.80). A power analysis can help determine the minimum sample size necessary to detect an effect if one exists. Generally, larger sample sizes yield more reliable results, but practical considerations like time and resources also play a significant role in determining how many people to test. Aim for a balance between statistical rigor and feasibility.
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i dont no but i need the anwser Yes an experiment will test a theory. You perform an experiment to test the hypothesis. If the experiment can be repeated then the hypothesis becomes a theory. People perform experiments to test and retest theories.
No, origianl hypotheses usually before the experiment that is the reason people do the experient which is test their hypotheses
That depends on the result of the experiment. The experiment is a way to test a hypothesis, and it's completely fine if the experiment disproves the hypothesis. Ideally, though, the experiment will support the hypothesis.
The experiment that you will design is done to test the hypothesis.
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i dont no but i need the anwser Yes an experiment will test a theory. You perform an experiment to test the hypothesis. If the experiment can be repeated then the hypothesis becomes a theory. People perform experiments to test and retest theories.
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how many times did you trial your experiment, for each test you did
The experiment is what you test and how you test it. Your entire project is based on the experiment.
No, origianl hypotheses usually before the experiment that is the reason people do the experient which is test their hypotheses
That depends on the result of the experiment. The experiment is a way to test a hypothesis, and it's completely fine if the experiment disproves the hypothesis. Ideally, though, the experiment will support the hypothesis.
In an experiment that involves many people or animals, it is important to test many individuals to avoid experimental error. If only one individual were to be tested in the experiment, it would be difficult to say whether the results were a product of the test, or if it was just a result that the particular individual produced. By testing many, scientists can say definitively that their hypothesis was correct or incorrect because a wide variety of test subjects responded in the same way.
The test charge is positive in the experiment.
In an experiment that involves many people or animals, it is important to test many individuals to avoid experimental error. If only one individual were to be tested in the experiment, it would be difficult to say whether the results were a product of the test, or if it was just a result that the particular individual produced. By testing many, scientists can say definitively that their hypothesis was correct or incorrect because a wide variety of test subjects responded in the same way.
It is usually recommended to test one variable at a time in an experiment to accurately determine its effect. This helps to isolate the impact of that specific variable and avoid confounding results from multiple factors changing simultaneously.
There are likely many, the beauty of experimentation is that there are many ways to test theories.