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I would like to discuss about point number 5 on Page 341.
“A large P value is evidence in favor of the test hypothesis.”
The statement is false. In hypothesis testing, the p-value represents the probability of obtaining a result equal to or more than the observed result, assuming the null hypothesis is true. If the p-value is small, typically less than the chosen significance level (often 0.05), we reject the null hypothesis.
If the p-value is large, it suggests that the observed result is likely to occur by chance, even if the null hypothesis is true. Consequently, we fail to reject the null hypothesis and do not have sufficient evidence to support the alternative hypothesis.
However, we should not conclude that the null hypothesis is true only based on a large p-value. Other factors such as sample size and potential sources of bias should also be considered when interpreting the results of a hypothesis test.
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