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A p-value is calculated using a statistical test, which compares the observed data against a distribution expected under the null hypothesis.

The common cutoff for declaring statistical significance is a p-value less than 0.05.

A p-value does not directly measure effect size; it addresses only the strength of evidence against the null hypothesis. Effect size needs to be measured separately to understand the magnitude of an effect.

A p-value is the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct.

The null hypothesis is a general statement or default position that there is no relationship between two measured phenomena. P-values are calculated with the assumption that the null hypothesis is true.

Yes, it is possible because a low p-value indicates statistical significance, not necessarily clinical or practical significance. The effect size must be evaluated to assess clinical relevance.

A low p-value suggests that the observed data are unlikely under the assumption of the null hypothesis, hence the result is considered to be statistically significant.

No, a p-value cannot provide this probability. It only indicates the probability of observed (or more extreme) data assuming the null hypothesis is true.

A larger sample size can lead to smaller p-values as it reduces variability and allows for a more accurate estimation of the effect size, potentially detecting smaller differences as statistically significant.

This practice, known as 'p-hacking', can lead to false positives, as it capitalizes on chance and inflates the likelihood of finding a statistically significant result by sheer repetition.

If the confidence interval for a parameter estimate does not include the value under the null hypothesis (e.g., a mean difference of zero), it corresponds to a statistically significant p-value (typically less than 0.05).

P-values can be calculated for one-tailed or two-tailed tests. In one-tailed tests, the direction matters, while in two-tailed tests, it does not, as they measure the extremeness of the effect regardless of direction.

A p-value does not indicate the size of an effect, the importance of a result, or the probability that the null hypothesis is true.

Setting the significance level too high (e.g., $p > 0.10$) increases the risk of Type I errors, falsely declaring a result as significant when it is actually due to chance.