The Importance Of P-Value In Hypothesis Testing: A Statistical Guide


~ The probability of finding the observed, or more extreme, results when the null hypothesis (H 0) of a study question is true

P-value is a statistical measure that tells us how likely it is to observe a certain result or more extreme results when conducting a hypothesis test, assuming that the null hypothesis is true. In other words, it measures the strength of evidence against the null hypothesis.

If the p-value is less than or equal to the chosen significance level (usually 0.05), then we reject the null hypothesis and conclude that there is enough evidence to support the alternative hypothesis. On the other hand, if the p-value is greater than the significance level, then we fail to reject the null hypothesis and conclude that there is not enough evidence to support the alternative hypothesis.

For instance, suppose we want to test the claim that the average height of high school students is greater than 5 feet. We collect a sample of students and calculate the sample mean. We can then use a hypothesis test to determine whether the data provides enough evidence to support this claim. The resulting p-value will help us decide whether to reject or accept the null hypothesis that the average height of students is 5 feet or less.

It’s important to note that the p-value alone cannot provide conclusive evidence against the null hypothesis, but rather it is used in conjunction with other statistical measures to make an informed decision.

More Answers:
Mastering Variables In Programming: Definition, Syntax And Applications
The Uniform Distribution Of P-Values When The Null Hypothesis Is True.
Statistical Significance: Analyzing Data To Determine Real Effects And Reject Null Hypotheses

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