Final answer:
The extent to which observed frequencies align with expected frequencies is known as a) Goodness of fit. It uses a chi-square test to determine if data fits a particular distribution, and the test is usually right-tailed.
Step-by-step explanation:
Refers to the extent to how closely the observed frequencies fit the expected frequencies is defined as option a) Goodness of fit. This statistical measure is used to analyze how well the observed data match the expected data if a certain hypothesis is true. In the context of Goodness-of-Fit tests, which utilize the chi-square distribution, the null hypothesis posits that the observed frequencies come from a certain assumed distribution.
The chi-square test for Goodness-of-Fit is almost always right-tailed. This means if the observed values significantly differ from the expected ones, the test statistic becomes large, and the null hypothesis is likely to be rejected. It's important for each expected frequency to be at least five in order to validate the use of the test.
In conclusion, the Goodness-of-Fit test is a crucial statistical tool used to ascertain whether a data set's observed frequencies are in line with what would be expected under a particular distribution, such as binomial, uniform, or normal.