Chi-square tests are powerful tools for analyzing categorical data. They help us determine if observed frequencies match expected patterns or if there's a relationship between variables. These tests are crucial in hypothesis testing, allowing us to make informed decisions based on data.

Goodness-of-fit tests check if data fits a specific distribution, while independence tests examine relationships between variables. Both use the chi-square statistic and distribution, with results interpreted through p-values and effect sizes. Understanding these tests is key for analyzing categorical data effectively.

Chi-square test assumptions

Key characteristics and applications

• Chi-square tests analyze categorical data and test hypotheses about frequency distributions as non-parametric statistical methods
• Two main types include goodness-of-fit test and test of independence used for distinct applications in statistical analysis
• Chi-square distribution remains right-skewed and non-negative with shape determined by degrees of freedom
• Widely used in various fields (biology, psychology, social sciences) to analyze survey data, genetic studies, and contingency tables

Important considerations

• Observations must be independent of each other
• Sample size should be sufficiently large (expected frequencies typically at least 5 in each cell)
• Sensitive to sample size leading to statistically significant results for small differences with very large samples
• Expected frequencies derived from hypothesized distribution or population proportions require clear justification in analysis

Goodness-of-fit tests

Test statistic and degrees of freedom

• Determines if sample data fits hypothesized distribution or if significant differences exist between observed and expected frequencies
• Test statistic calculated as sum of $\frac{(Observed - Expected)^2}{Expected}$ for all categories
• Degrees of freedom calculated as $(k - 1)$ where k represents number of categories
• Critical value determined by chosen significance level (α) and degrees of freedom using chi-square distribution table or statistical software

Hypothesis testing and interpretation

• Null hypothesis states observed frequencies match expected frequencies
• Alternative hypothesis suggests significant difference exists
• Reject null hypothesis if p-value less than predetermined significance level (α)
• Interpret results based on how well observed data fits expected distribution or if significant deviations exist
• Effect size measures (Cramer's V) assess strength of relationship between variables in addition to significance test

Chi-square test of independence

Test statistic and contingency tables

• Determines significant relationship between two categorical variables in contingency table
• Test statistic calculated similarly to goodness-of-fit test
• Expected frequencies derived from row and column totals: $\frac{(row total column total)}{grand total}$
• Degrees of freedom calculated as $(r - 1)(c - 1)$ where r represents number of rows and c represents number of columns in contingency table

Advanced analysis techniques

• Standardized residuals identify specific cells in contingency table contributing most to overall chi-square statistic
• Post-hoc analysis (pairwise comparisons with adjusted p-values) necessary for contingency tables larger than 2x2
• Strength of association measured using Cramer's V or phi coefficient depending on size of contingency table

Interpreting chi-square results

Statistical interpretation

• Chi-square statistic quantifies overall difference between observed and expected frequencies with larger values indicating greater discrepancies
• P-value represents probability of obtaining chi-square statistic as extreme as or more extreme than observed value assuming null hypothesis true
• Reject null hypothesis if p-value less than predetermined significance level (α) suggesting significant difference or relationship exists

Reporting and practical considerations

• Include chi-square statistic, degrees of freedom, p-value, and effect size measure when reporting results ($χ^2(df) = value, p = value, Cramer's V = value$)
• Consider both statistical significance and practical importance as large sample sizes can lead to statistically significant results for small differences
• For goodness-of-fit tests interpret how well observed data fits expected distribution or if significant deviations exist
• For tests of independence interpret presence or absence of significant relationship between two categorical variables and nature of that relationship