Data Analysis
Practical workflows for exploring, cleaning, and interpreting real datasets, from the first look at the data through to a conclusion you can defend.
Visit the topic hubStatistics, made usable
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Core notation, vocabulary, and concepts for learning statistics, covering populations versus samples, variable types, and summation notation.
Core summaries for describing datasets: mean, median, and mode for center; variance, standard deviation, and range for spread; and skewness for shape.
Probability rules, from single events to conditional probability, plus the normal, binomial, and Poisson distributions that model random outcomes.
How variables relate to each other: correlation coefficients, lines of best fit, and linear regression for measuring an association's strength.
Hypothesis tests, confidence intervals, p-values, and the reasoning for drawing conclusions about a population from a limited sample.
Applied statistics
Explore practical topic hubs now; in-depth Applied guides will appear here as they are published.
Showing all 4 Applied Statistics topics.
Practical workflows for exploring, cleaning, and interpreting real datasets, from the first look at the data through to a conclusion you can defend.
Visit the topic hubHow to design A/B tests and experiments that answer the question you asked, and how to separate genuine cause from coincidence in observational data.
Visit the topic hubBuilding and validating forecasts from time-ordered data: trend, seasonality, backtesting, and recognising when a model has quietly stopped working.
Visit the topic hubThe statistical thinking behind machine learning: validation, overfitting, class imbalance, and reading model metrics without fooling yourself.
Visit the topic hubPopular calculators
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