Certified Histocompatibility Specialist Practice Test 2026 - Free Practice Questions and Study Guide

The statement that chi-square becomes unreliable with small expected values is accurate and highlights an important aspect of the chi-square test. The chi-square test is used in hypothesis testing to determine whether there is a significant association between categorical variables. However, for the chi-square statistic to be valid, certain assumptions must be met, particularly regarding the expected frequencies in each category.

When the expected frequencies are too low (typically less than 5), the chi-square approximation to the distribution may not hold, leading to unreliable results. In these cases, the test statistic may not accurately reflect the true significance, which can lead to inappropriate conclusions about the relationship between the variables being analyzed. Thus, it is important to ensure that the expected counts in each category are sufficiently large for the chi-square test to yield valid results.

In contrast, other statements may misrepresent the properties of the chi-square test, such as its application to numerical versus categorical data, or the conditions under which certain corrections like Yates' are employed.