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The point that I will select for the discussion will be Point 3: “A significant test result (P value <= 0.05) means that the test hypothesis is false or should be rejected” from page 341.
This point clarifies that just because a study shows a significant result (small P value), it doesn’t mean that the test hypothesis is wrong and should be dismissed. Instead, it can be suggested that the observed data is unusual if all the assumptions used in the statistical test were correct.
While a result is significant and can indicate the potential effect, it doesn’t provide conclusive evidence. The P value is just a single aspect of data that must be analyzed alongside additional details before concluding the test hypothesis. The significant result could have been caused by a variety of factors such as random chance, errors in the study design, or biases that may cause discrepancies in the data.
Reference: Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN, Altman DG. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016 Apr;31(4):337-50. doi: 10.1007/s10654-016-0149-3. Epub 2016 May 21. PMID: 27209009; PMCID: PMC4877414.