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Biostatistics
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MOL.824UB Biostatistics

From describing data to drawing conclusions. Explore the ideas behind biostatistics through worked examples, simulations, and interactive plots.

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Six chapters · Start anywhere

01Describe

Descriptive statistics

Make sense of a dataset with distributions, means, medians, quantiles, and measures of spread. Explore outliers, effective visualizations, and standardized effect sizes.
02Estimate

Inferential statistics

Connect samples to populations using probability models, sampling distributions, and standard errors. Investigate confidence intervals, bootstrapping, and uncertainty propagation.
03Model

Correlation and regression

Explore relationships between variables with Pearson and Spearman correlation and least-squares regression. Examine prediction, residuals, outliers, and confounding.
04Test · Foundations

Hypothesis testing: foundations

Build an understanding of null models, p-values, and one- and two-sided tests. Explore confidence intervals, Type I and Type II errors, statistical power, and common misinterpretations.
05Test · Applications

Hypothesis testing: examples

Choose and apply tests for independent groups, paired measurements, counts, and ranks. Work through ANOVA, interactions, multiple comparisons, and checks of model assumptions.
06Update

Bayesian statistics

Update uncertainty with Bayes’ theorem, from diagnostic tests to inference for a proportion. Explore priors, posteriors, prediction, MCMC sampling, and Bayesian linear regression.

MOL.824UB Biostatistics · University of Graz

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