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Correct Answer: statistical significance
statistical significance is a term used in statistical hypothesis testing to determine whether the observed data deviate from what is expected under a hypothesis of no effect or no difference. essentially, it helps to quantify whether a result is likely due to chance or some factor of interest.
when researchers propose a theory or hypothesis, they use experiments or studies to collect data to support or refute their theory. statistical significance plays a critical role here by providing a metric to evaluate the reliability of the observed effects. for instance, if a study is testing whether a new drug is more effective than an existing one, statistical significance can help determine whether any differences in outcomes between the drug groups are meaningful or just due to random variation.
the determination of statistical significance is typically made using a p-value, which measures the probability of observing an effect at least as extreme as the one in your data, assuming the null hypothesis is true. a commonly used threshold for declaring statistical significance is a p-value of less than 0.05. if the p-value falls below this threshold, researchers may reject the null hypothesis, suggesting that the observed data exhibit a genuine effect and are not a result of random chance.
in contrast, a control group in experiments serves as a baseline to compare the effects of the variable being tested. variables refer to any characteristics, quantities, or influences that can be measured or manipulated in an experiment. the null hypothesis, on the other hand, typically asserts that there is no effect or no difference between groups or variables, serving as a statement or idea that a researcher seeks to invalidate through testing.
thus, statistical significance is crucial for validating a theory or hypothesis as it statistically ascertains whether the patterns seen in the data can reliably be attributed to the effect being tested rather than to random variability. this supports the researcher's ability to draw meaningful conclusions from the experiment, distinguishing real effects from those occurring by chance.
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