Which statement is true about hypotheses?

The statement regarding the null hypothesis being rejected if the alternative hypothesis is accepted accurately reflects the process of hypothesis testing in statistics. In hypothesis testing, researchers start with a null hypothesis, which typically posits that there is no effect or no difference in the context of the study. The alternative hypothesis suggests that there is an effect or a difference.

When conducting a statistical test, researchers calculate a p-value based on the sample data. If the p-value is less than a predetermined significance level (commonly set at 0.05), the evidence is considered strong enough to reject the null hypothesis in favor of the alternative hypothesis. Thus, accepting the alternative hypothesis is predicated upon rejecting the null hypothesis, which is inherently a crucial step in establishing that the observations in the data are statistically significant.

This understanding is fundamental in the realm of research and statistics, as it underscores the reasoning behind causal inferences and the validity of conclusions drawn from empirical data. Therefore, recognizing that rejecting the null leads to accepting the alternative is a vital aspect of interpreting statistical results accurately.