What P-value is
The P-value is a statistical measure that helps researchers determine whether or not their results are statistically significant. It is used to decide whether to reject or accept the null hypothesis in a statistical test.
P-values are calculated using the following steps:
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State the null and alternative hypotheses. The null hypothesis is the hypothesis that there is no significant difference between the groups being compared, while the alternative hypothesis is the hypothesis that there is a significant difference between the groups.
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Calculate the test statistic. This is the measure used to compare the null and alternative hypotheses. It is usually a measure of the difference between the two groups, such as the difference in means or proportions.
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Calculate the P-value. This is the probability of obtaining a result as extreme as the observed result, given that the null hypothesis is true.
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Compare the P-value to the significance level. The significance level is usually set at 0.05, meaning that if the P-value is less than 0.05, it is considered statistically significant.
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Make a decision. If the P-value is less than the significance level, then the null hypothesis can be rejected and the alternative hypothesis accepted. If the P-value is greater than the significance level, then the null hypothesis cannot be rejected.
Examples
- A P-value is used to determine whether a result from a hypothesis test is statistically significant.
- P-values are often used to make decisions about model fit and the validity of a hypothesis.
- In regression analysis, a P-value is used to determine whether a predictor variable is statistically significant.
- P-values are used in ANOVA to determine if the difference between two or more groups is statistically significant.
- P-values are used to determine the probability that a given sample is not representative of the population.