Understanding Clinical Research: Behind the Statistics

This module provides an intuitive understanding of clinical research results, focusing on research methods and data collection with a specific focus on study types. It covers observational and experimental studies, case series, case-control, cross-sectional, cohort studies, meta-analysis, and systematic reviews.

This module focuses on types of data and statistical tests used in healthcare research. It covers nominal and ordinal categorical data, numerical data types, measures of central tendency and dispersion, sampling methods, and how to set up spreadsheets for analysis.

This module covers p-values, probability theory, the Central Limit Theorem, data distribution, skewness and kurtosis, normal distribution, sampling distribution, and t-distribution. It aims to build an intuitive understanding of these statistical concepts.

This module focuses on hypothesis testing, confidence intervals, and common statistical tests used in healthcare research. It covers null and alternative hypotheses, type I and II errors, confidence levels, and introduces t-tests, analysis of variance, and linear regression.

This module delves deeper into parametric and non-parametric tests, their assumptions, and when to use them. It covers Student's t-test, ANOVA, linear regression, and various non-parametric tests such as sign test, Wilcoxon signed-rank test, and Mann-Whitney U test.

This final module covers the analysis of categorical data, including contingency tables, chi-square test, and Fisher's exact test. It also introduces sensitivity, specificity, positive and negative predictive values, and risk and odds ratios.