Final answer:
The p-value is computed assuming the null hypothesis is true. It helps to decide whether to reject or maintain the null hypothesis based on how small it is compared to the predetermined level of significance.
Step-by-step explanation:
The p-value is a probability that is computed under the assumption that the null hypothesis is true. It represents the probability of obtaining a result at least as extreme as the one observed, purely by chance, if the null hypothesis were true. When conducting a hypothesis test, we use the p-value to make a decision about the null hypothesis. If the p-value is very small, it suggests that the observed result would be highly unlikely to occur if the null hypothesis were true. In such cases, it provides strong evidence against the null hypothesis, leading to its rejection in favor of the alternative hypothesis.
A p-value that is lower than the set level of significance of the test (typically 0.05 or 0.01) indicates that it is appropriate to reject the null hypothesis. Conversely, a larger p-value suggests that there is insufficient evidence to reject the null hypothesis. It's essential to remember that a low p-value doesn't prove the alternative hypothesis; it just indicates that the data are inconsistent with the null hypothesis.
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