Mastering Biostatistics: Step-by-Step

Explore study design in biostatistics by contrasting observational and experimental methods, including cross-sectional, case-control, and cohort studies, and examining randomized controlled trials and blinding to establish causation while minimizing bias.

Learn to select representative samples using random, stratified, systematic, and cluster methods, minimize sampling bias, and ensure reliable, valid biostatistics conclusions.

Explore the central limit theorem, its role in biostatistics, and how sampling means form a normal distribution, enabling confidence intervals and hypothesis testing.

Explore descriptive statistics in biostatistics by calculating and interpreting central tendency (mean, median, mode), dispersion (range, variance, standard deviation), and visualizing data with histograms and box plots, including outliers.

Explore measures of central tendency by learning how to calculate and interpret the mean, median, and mode, with examples like blood pressure, cholesterol, and salaries, including outliers and skewed data.

Explore probability as the foundation of biostatistical inference, measuring uncertainty and predicting outcomes, and learn independence, conditional probability, normal, binomial, Poisson, exponential distributions, and the central limit theorem.

Explore the normal (bell curve), binomial, Poisson, and exponential distributions in biostatistics, and learn to identify and apply them to real-world medical data.

Mastering biostatistics shows how to construct and interpret confidence intervals for population parameters from sample data. Apply standard error and z scores to estimate means and assess precision.

Explore the t test, chi squared test, and Anova to compare means and analyze categorical data across one way Anova and two way Anova designs, with post-hoc options.

Learn nonparametric tests as alternatives to t tests and ANOVA for non-normal data, outliers, and small samples, including Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed rank test.

Learn to set up null and alternative hypotheses, assess type I and II errors, and decide significance using p values and alpha levels in biostatistics.

Explore simple and multiple linear regression in biostatistics, learn model assumptions, and interpret coefficients, r-squared, and p-values to predict health outcomes.

Explore logistic regression for binary outcomes by modeling log odds to predict disease risk, and use odds ratios to quantify predictor effects.

Learn how Bayesian inference updates probabilities with new data by combining prior knowledge, likelihood, and posterior to inform medical decision making, adaptive trials, and disease modeling in biostatistics.

Explore how to calculate measures of central tendency—mean, median, and mode—using a real biostatistics data set in RStudio with tidyverse tools.

Measure the spread of data using range, variance, standard deviation, and interquartile range in RStudio, applied to cholesterol levels with Tidyverse workflows and summary outputs.

Apply a chi square test to assess whether smoking status and treatment group are related, using a contingency table and p value above 0.05 to conclude independence.

perform a one-way anova to compare blood pressure reduction across control, drug a, and drug b, and use a box plot with tukey post hoc test.

Explore correlation analysis between body mass index (BMI) and cholesterol using a scatter plot, Pearson's r, and a regression line, showing a moderate positive relationship.

Build a multiple linear regression model to predict cholesterol from age and BMI, interpret coefficients and r-squared 0.61, and visualize the fit with a regression line and confidence interval.

Compare two independent groups with nonparametric methods using box plots and the Wilcoxon rank sum test to assess median differences and p-values when data are not normally distributed.

Learn to perform survival analysis in RStudio, handle censoring, and interpret Kaplan-Meier curves to compare time-to-event between treatment groups.

Apply biostatistical thinking to real-world problems by analyzing data with sampling, describing data with techniques, assessing uncertainty, testing ideas, predicting outcomes, exploring relationships between variables, and visualizing results in R.