Learn Statistics Daily!

Learn the basics of statistics, including population and sample, descriptive and inferential statistics, and the roles of parameters, statistics, and sampling error.

Understand how researchers use correlational, experimental, and quasi experimental designs to test causality, with manipulation, control, and attention to confounding variables, while distinguishing nominal, ordinal, interval, and ratio scales.

Explore how to describe data distributions using descriptive statistics, frequency distributions, and graphs, and learn to interpret rank and percentile in normal and skewed distributions.

Explore central tendency, learning how the mean, median, and mode summarize data, compare distributions, and decide when to use weighted mean or percentile for accurate summaries.

Analyze data variability using range, interquartile range, and standard deviation to describe distributions, compare population and sample variance and sum of squares, and interpret box plots with quartiles.

Learn how z scores use the mean and standard deviation to locate any score within a distribution and enable cross-variable comparisons through standardization.

Explore how probability links samples to populations, enabling inferential statistics to infer population parameters from samples, using random sampling, sampling with replacement, and normal and binomial distributions.

Explore the distribution of sample means, standard error, and z scores, and apply the central limit theorem to predict population means and sample probabilities.

Learn how to conduct hypothesis testing by formulating null and alternative hypotheses, choosing an alpha level, and using sample means to assess significance with p values.

Compare hypothesis testing and estimation, noting that statistical significance does not imply practical importance, and measure effects with point estimates, interval estimates, confidence intervals, and Cohen's d.

Expose how statistics can mislead through graphs and visuals - understanding, clarity, consistency, efficiency, necessity, truthfulness - while naming common tricks like biased scales and truncation.

Learn how biostatistics underpins medical decision making by evaluating validity, reliability, sensitivity, specificity, and predictive values, and use ROC curves and prevalence to interpret screening and diagnosis.

Identify how bias and confounding distort study results by examining selection, allocation, measurement, and recall biases. Control confounding through restriction, matching, stratification, and standardization, and emphasize randomization to reduce bias.

Learn how correlation measures the strength and direction of linear relationships with scatterplots and Pearson's r, and how regression uses a best-fit line for prediction, including multiple regression and outliers.

Explore non-parametric tests as distribution-free alternatives to parametric methods, including chi-square, Fisher's exact test, Mann-Whitney U, and Wilcoxon tests, plus Spearman correlation and exact tests for small samples.