The p value is defined as the probability of observing the given value of the test statistic, or greater, under the null hypothesis. Traditionally, the cut-off value to reject the null hypothesis is 0. Now let us go back to our case: we are comparing means and assuming that the data is normally distributed, so we use a t-test and compute a t-statistic of 2. Because we use a 0. Because this is a very small probability, we reject the null hypothesis. Looking at the p value alone devi-ates our attention from the effect size.
In our example, the p value is significant but a drug that increases urine output by 45 mL has no clinical relevance. The p value indicates the probability of ob-serving a difference as large or larger than what was observed, under the null hypothesis.
But if the new treatment has an effect of smaller size, a study with a small sample may be underpowered to detect it. It is inappropriate to interpret a p value of, say, 0. A p value of 0. Effect sizes versus p values Many researchers believe that the p value is the most important number to report. However, we should focus on the effect size.
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