Statistical intuition and literacy for everyone, built on real stories, real data, and everyday reasoning.
What statistics is, why data varies, populations vs samples, variable types, and how we collect data reliably without measuring everyone.
Frequency tables, bar charts, histograms, frequency polygons, and cumulative curves. Turning a pile of numbers into a picture that tells a story.
Mean, median, and mode for raw and grouped data. When each measure is the right choice, and when the "average" is the most misleading number in the room.
Range, IQR, variance, standard deviation, CV, moments, skewness, and kurtosis. Two batsmen with the same average can be completely different players.
Scatter plots, Pearson's r, interpreting strength and direction, and the most common trap in statistics: mistaking correlation for causation.
The regression line, what the slope and intercept mean in plain English, prediction, and what R-squared actually tells you about a model.
When the outcome isn't a plain number: predicting yes/no outcomes with logistic regression and predicting event counts with Poisson regression.
Sample spaces, events, probability rules, conditional probability, and independence. Why a positive medical test does not mean what most people think it means.
Bernoulli trials, the binomial model, mean and variance. Counting successes when every outcome is either yes or no, and nothing in between.
Waiting for the r-th success. When the question shifts from "how many successes?" to "how long until we get enough?", the negative binomial takes over.
Modeling counts of rare events over time or space. Hospital arrivals, road accidents, and flood occurrences, when events happen randomly but at a predictable average rate.
The bell curve, the empirical rule, z-scores, and the standard normal table. Why this one shape appears in exam scores, heights, and measurement errors everywhere.
Time between events, the memoryless property, and the link back to Poisson. How long will you wait for the next bus if buses arrive randomly at a known average rate?
Why sample means vary, what standard error means, and the remarkable fact that averages become normal no matter what the original data looked like.
Point estimates, interval estimates, and the margin of error. What "54% support, plus or minus 3%" actually means, and what it does not mean.
Null and alternative hypotheses, test statistics, p-values, and Type I and Type II errors. How scientists decide whether a result is real or just noise.
Comparing means across three or more groups with the F-test. When a t-test is no longer enough and you need to ask which of these groups is actually different.