How to Interpret P-Values in H2 Math Hypothesis Testing

How to Interpret P-Values in H2 Math Hypothesis Testing

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Frequently Asked Questions

A p-value is the probability of obtaining test results at least as extreme as the results actually observed during the test, assuming that the null hypothesis is correct. It helps determine the statistical significance of the results.
Generally, if the p-value is less than or equal to the significance level (alpha), typically 0.05, we reject the null hypothesis. This suggests that there is sufficient evidence to support the alternative hypothesis. If the p-value is greater than alpha, we fail to reject the null hypothesis.
A small p-value (e.g., p < 0.05) suggests strong evidence against the null hypothesis. It indicates that the observed results are unlikely to have occurred if the null hypothesis were true, leading to the rejection of the null hypothesis.
A large p-value (e.g., p > 0.05) suggests weak evidence against the null hypothesis. It indicates that the observed results are reasonably likely to have occurred even if the null hypothesis were true, leading to a failure to reject the null hypothesis.
The significance level (alpha), often set at 0.05, is the threshold for determining statistical significance. It represents the probability of rejecting the null hypothesis when it is actually true (Type I error).
No, the p-value does not prove that the alternative hypothesis is true. It only provides evidence to either reject or fail to reject the null hypothesis. Failing to reject the null hypothesis does not mean it is true, just that there isnt enough evidence to reject it.
The p-value helps in making informed decisions by quantifying the strength of the evidence against the null hypothesis. In real-world scenarios, this could involve deciding whether a new drug is effective, whether a marketing campaign increased sales, or whether there is a significant difference between two populations.