Final answer:
The statement in question is false. A 95% confidence interval means that if we were to take many samples and create confidence intervals from them, about 95% would contain the true population parameter. It doesn't mean that each sample statistic will match the population value.
Step-by-step explanation:
The statement, 'The chance that a specific sample statistic matches the population value is usually set to be 95%' is false. The concept being referred to here is the confidence interval. A confidence interval is an interval estimate of a population parameter and provides a range of values within which the parameter is estimated to fall. The likelihood that the confidence interval contains the population parameter (such as the mean) is called the confidence level. When we say a 95% confidence interval, it means that if we were to take repeated samples and construct confidence intervals from these samples, approximately 95% of these intervals would contain the true population parameter.
Therefore, sampling distribution concepts typically involve the construction of confidence intervals around sample statistics, like the mean, and do not guarantee that any specific statistic matches the population value exactly. Instead, it describes the probability that the range contains the population value. Statements like 'If we took repeated samples, approximately 90 percent of the confidence intervals calculated from those samples would contain the true value of the population mean' illustrate this principle, with 90% being an example if a 90% confidence level was chosen.