Statistical Methods Every Data Scientist Should Know

Statistics is the toolkit that turns raw numbers into insight. For a data scientist, knowing a few core methods helps you answer questions clearly, avoid errors, and share results with confidence. This guide covers practical methods you can apply in real projects.

Descriptive statistics and probability

Descriptive stats describe data at a glance: mean, median, mode, and spread. Visual checks like histograms or box plots accompany the numbers. A quick example: exam scores cluster around 70–80 with a standard deviation near 8.

  • Mean, median, and mode
  • Variance and standard deviation
  • Distribution shapes (normal, skewed)
  • Percentiles and interquartile range

Probability theory helps you model uncertainty and plan experiments. Simple rules, like the idea that outcomes add up to 1, keep reasoning clear when you collect more data.

Inferential statistics

Inferential methods let you generalize from a sample to a population. Hypothesis tests compare groups; p-values indicate how unlikely observed differences are if there is no real effect. Confidence intervals show a plausible range for the true value at a chosen level, usually 95%.

  • Hypothesis testing (null vs. alternative)
  • P-values and significance
  • Confidence intervals
  • t-tests and ANOVA for group comparisons

Modeling and prediction

Prediction relies on models that explain data and forecast new cases. Start with simple relationships, then add structure to capture more patterns.

  • Regression: linear and logistic
  • Assumptions: linearity, independence, homoscedasticity
  • Regularization: ridge and lasso
  • Model validation: cross-validation and train/test split

Resampling and uncertainty

Resampling methods quantify uncertainty without heavy theory. They are practical for real data work.

  • Bootstrapping
  • Cross-validation variants (K-fold, stratified)
  • Monte Carlo simulations

Bayesian thinking

Bayesian statistics blend prior knowledge with data to form a posterior view. This approach helps update beliefs as new information arrives.

  • Priors, likelihood, posterior
  • Credible intervals
  • Prior predictive checks

Practical tips

  • Start simple, then add complexity as needed.
  • Check model assumptions and diagnose issues with residuals.
  • Report uncertainty clearly, not just point estimates.
  • Keep analyses reproducible and document choices.

Key Takeaways

  • A solid foundation in descriptive, inferential, and predictive stats anchors good data work.
  • Be mindful of uncertainty, model assumptions, and validation.
  • Use Bayesian ideas when prior knowledge matters, and rely on resampling to quantify risk.