Final answer:
The comparison of the sample statistic to the population value prior to a study is called Hypothesis testing. It is integral to inferential statistics, where it helps determine if sample data significantly differ from the population parameter or if any observed difference is due to chance. So the correct answer is option A.
Step-by-step explanation:
The comparison of the sample statistic with what is believed to be the population value prior to undertaking the study is called A) Hypothesis testing. This process involves collecting data from a sample and evaluating it to make a decision about the claims concerning the population. The statistician looks for evidence to either reject or fail to reject the null hypothesis based on the analysis of the sample data.
In the broader field of inferential statistics, we use sample data to estimate a population parameter, such as a mean or a proportion. Constructing interval estimates like confidence intervals helps us understand the range within which the population parameter is likely to fall.
A hypothesis test, especially when comparing proportions or means, helps determine if observed differences are due to actual population differences or merely by chance. If a rare event is observed, it might indicate that the null hypothesis is false. The t-test is a common method used in hypothesis testing, evaluating if the sample mean is significantly different from the known population mean or comparing means of two different groups.