Chi-squared test
Chi-squared test
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Introduction to biostatistics
Types of data
Probability
Mean, median, and mode
Range, variance, and standard deviation
Standard error of the mean (Central limit theorem)
Normal distribution and z-scores
Paired t-test
Two-sample t-test
Hypothesis testing: One-tailed and two-tailed tests
One-way ANOVA
Two-way ANOVA
Repeated measures ANOVA
Correlation
Methods of regression analysis
Linear regression
Logistic regression
Spearman's rank correlation coefficient
Mann-Whitney U test
Kappa coefficient
Chi-squared test
Fisher's exact test
Kaplan-Meier survival analysis
Type I and type II errors
Sensitivity and specificity
Positive and negative predictive value
Test precision and accuracy
Incidence and prevalence
Relative and absolute risk
Odds ratio
Attributable risk (AR)
Mortality rates and case-fatality
DALY and QALY
Direct standardization
Indirect standardization
Study designs
Clinical trials
Disease causality
Selection bias
Confounding
Interaction
Bias in interpreting results of clinical studies
Bias in performing clinical studies
Prevention
Key Takeaways
The chi-squared test is a statistical method used to test the independence of two categorical variables. The chi-squared statistic is calculated as the sum of the squared differences between the observed and expected frequencies for each category, divided by the number of degrees of freedom.