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distributions
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Mean, median or quantile: which point prediction should a model give?
Why the loss you will be judged on, not the likelihood you fitted, decides whether a point prediction should be the conditional mean, the median or a quantile.
Read the answer →Why does a t test use the t distribution and ANOVA the F distribution?
How the t, F and chi-square reference distributions are derived from the way each test statistic is built, and what to do when no formula exists.
Read the answer →Where should histogram bin edges go when your data are rounded?
When values are recorded to a fixed unit, put histogram bin edges halfway between the possible values. Here is why, and how counts differ.
Read the answer →Skewed data with zeros: is log(x + 1) OK, and what else works?
Why log(x + c) gives results that hinge on an arbitrary constant, and better options for skewed data with zeros: log-link GLMs, two-part models and asinh.
Read the answer →Kolmogorov-Smirnov, t test or Mann-Whitney: which detects a change from a baseline?
What each two-sample test detects, why Kolmogorov-Smirnov misses changes in spread with small samples, and how to compare against a baseline.
Read the answer →Why does the central limit theorem work? An intuitive explanation
Averages look normal because averaging erases every feature of the original shape except its center and spread. The idea, without heavy math.
Read the answer →Log-transforming data: when it helps, and what it changes
Logs turn multiplicative, right-skewed data into additive, roughly symmetric data. Here is when that helps and how results translate back.
Read the answer →What is the beta distribution, and what is it used for?
The beta distribution describes uncertainty about a proportion or rate. Here is what its horizontal axis means and how its two parameters shape it.
Read the answer →What does a normal Q-Q plot's shape actually tell you?
A normal Q-Q plot compares your data to a normal distribution. The shape of the departure from the line tells you what kind of departure you have.
Read the answer →Is a normality test worth running on your data?
Normality tests answer the wrong question: with lots of data they flag harmless quirks, with little data they miss real problems. Here is what to do.
Read the answer →What does kurtosis actually tell you about your data?
Kurtosis describes how heavy a distribution's tails are, not how peaked it is. Here is how to read it, in plain language.
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