Fisher's exact test
Fisher's exact test
Biostatistics & Epidemiology
Biostatistics & Epidemiology
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
Ecologic study
Cross sectional study
Case-control study
Cohort study
Randomized control trial
Clinical trials
Sample size
Placebo effect and masking
Disease causality
Selection bias
Information bias
Confounding
Interaction
Bias in interpreting results of clinical studies
Bias in performing clinical studies
Modes of infectious disease transmission
Outbreak investigations
Disease surveillance
Vaccination and herd immunity
Prevention
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
Key Takeaways
Fisher's exact test is a statistical test that uses a 2x2 categorical data design. It is used to determine whether the proportions of two categorical variables are different from each other. It is a more powerful alternative to the chi-squared test, and is particularly useful when there are small sample sizes.