Starnes, The Practice of Statistics, 6e, Chapter 11

one-way table

Table used to display the distribution of a single categorical variable. (p. 709)

observed counts

Actual numbers of individuals in the sample that fall in each cell of the one-way or two-way table. (p. 711)

expected counts

Expected numbers of individuals in the sample that would fall in each cell of the one-way or two-way table if H₀ were true. (p. 711)

chi-square test statistic

Measure of how far the observed counts are from the expected counts. The formula is χ² = Σ(Observed Count - Expected Count)² ÷ Expected Count. (p. 712)

chi-square distribution

A distribution that is defined by a density curve that takes only non-negative values and is skewed to the right. A particular chi-square distribution is specified by giving its degrees of freedom. (p. 715)

Large Counts condition for a chi-square test

It is safe to use a chi-square distribution to perform calculations if all expected counts are at least 5. (p. 717)

chi-square test for goodness of fit

A test of the null hypothesis that a categorical variable has a specified distribution. (p. 718)

multiple comparisons

Problem of how to do many comparisons at once with an overall measure of confidence in all our conclusions. (p. 728)

chi-square test for homogeneity

A test of the null hypothesis that the distribution of a categorical variable is the same for two or more populations/treatments. (p. 737)

chi-square test for independence

A test of the null hypothesis that there is no association between two categorical variables in the population of interest. (p. 744)

components

Individual terms that are added together to produce the test statistic χ²: component = (Observed Count - Expected Count)² ÷ Expected Count (p. 752)