Types of Data in Biostatistics
A practical guide to types of data in biostatistics, including categorical, binary, nominal, ordinal, continuous, discrete, count, interval, ratio, time-to-event, longitudinal, and repeated-measures data, with clinical examples and statistical methods.
ANOVA for Comparing Multiple Treatment Groups
A practical guide to one-way ANOVA for comparing multiple treatment groups, including hypotheses, sums of squares, the F statistic, assumptions, a complete clinical-trial worked example, post hoc comparisons, effect sizes, R and SAS implementation, and common mistakes.
Understanding the Normal Distribution in Biostatistics
A practical guide to the normal distribution in biostatistics, including the bell-shaped curve, mean and standard deviation, standardization and z-scores, empirical rules, percentiles, clinical examples, confidence intervals, and common mistakes.
Understanding P-Values in Clinical Research
A practical guide to understanding p-values in clinical research, including null hypotheses, statistical significance, confidence intervals, effect sizes, multiplicity, power, subgroup analyses, and clinical interpretation.
Type I and Type II Errors in Hypothesis Testing
A practical guide to Type I and Type II errors in hypothesis testing, including false positives, false negatives, significance levels, power, sample size, clinical trial examples, and the relationship between alpha, beta, and statistical power.
Standard Error vs. Standard Deviation Explained
A practical guide to standard deviation versus standard error, including how each is calculated, what each measures, why standard error decreases with sample size, confidence intervals, hypothesis testing, and clinical research examples.
Skewness and Kurtosis in Clinical Data
A practical guide to skewness and kurtosis in clinical data, including interpretation of asymmetric distributions, tail behavior, outliers, mean versus median, clinical examples, formulas, R implementation, and implications for statistical analysis.
Permutation Tests for Clinical Endpoints
A practical guide to permutation tests for clinical endpoints, including the permutation principle, exact and Monte Carlo permutation tests, randomized treatment comparisons, p-values, exchangeability, covariate adjustment, survival endpoints, and R implementation.
Pearson vs. Spearman Correlation Coefficients
A practical guide to Pearson and Spearman correlation coefficients in clinical research, including when to use each method, assumptions, interpretation, confidence intervals, examples, ties, outliers, and R and SAS implementation.
Outlier Detection Methods in Clinical Datasets
A practical guide to detecting, investigating, and handling outliers in clinical datasets using graphical methods, IQR rules, robust statistics, z-scores, modified z-scores, clinical reference ranges, regression diagnostics, Mahalanobis distance, and R examples.