Which of the following reflects a smaller chance of error in hypothesis testing?

A small p-value reflects a smaller chance of error in hypothesis testing because it indicates strong evidence against the null hypothesis. In hypothesis testing, the null hypothesis generally represents a default position that there is no effect or no difference. When researchers calculate a p-value, they are assessing the probability of observing the data, or something more extreme, if the null hypothesis is true.

A small p-value (typically less than a significance level, such as 0.05) suggests that the observed data is very unlikely under the null hypothesis, leading researchers to consider rejecting the null hypothesis. This rejection implies that it is more likely that there is an actual effect or difference present. Consequently, a small p-value minimizes the risk of making a Type I error, which occurs when the null hypothesis is incorrectly rejected.

In contrast, a large p-value indicates weak evidence against the null hypothesis, and a moderate p-value does not provide strong enough evidence to confidently reject the null hypothesis. Similarly, while a near-zero p-value also suggests strong evidence against the null hypothesis, the question contextually leans towards the general understanding that a small p-value encompasses a threshold of statistical significance that significantly minimizes error risks in interpretations. Thus, a small p-value is recognized as indicating fewer chances