Statistics is how you tell the difference between a real pattern and random noise. This module starts with histograms and the shape of your data, then introduces the normal distribution, the bell curve that describes how many real-world measurements behave, and how the mean and standard deviation define it.
You'll then learn how to reason about samples: the standard error tells you how much a sample mean can vary, and confidence intervals turn that variation into a range of plausible values for the whole population. The module finishes with p-values and R-squared, so you can judge whether a difference is meaningful and how much of the variation a relationship explains.
With these ideas in place, you'll be ready for the next module, SQL Fundamentals, where you start pulling the data you want to analyse straight from a database.
How a histogram groups measurements into bins to show how often each value occurs, how bin width changes the picture, and why histograms are the first chart to reach for when exploring a variable.
Why normal curves are centred on the average, how the standard deviation sets their width, and how roughly 95% of measurements fall within two standard deviations of the mean.
The idea of repeated samples from the same population, what sampling with replacement means, and how the standard error measures how much a sample mean varies.
Why a sample mean is only an estimate of the population mean, and how a 95% confidence interval gives a reasonable range of values for it.
What the null hypothesis is, how to interpret a p-value and the 0.05 threshold, what a false positive looks like, and why a p-value does not tell you how big a difference is.
How R-squared describes the share of variation explained by the relationship between two variables, and why it is easier to interpret than the correlation coefficient.