Statistical methods in data science

Statistics help data scientists turn data into evidence. They quantify uncertainty, test ideas, and guide decisions. Good methods are simple to understand and careful to apply.

Core ideas

  • Uncertainty is natural. Probability gives a clear language to speak about chances.
  • Data quality matters. How data is collected, measured, and cleaned affects results.
  • Inference and prediction have different goals. Inference asks what happened; prediction asks what will happen next.

Common methods

Descriptive statistics summarize data with measures like the mean, median, and spread. Inferential tools use samples to learn about a larger group. Examples include hypothesis tests and confidence intervals. Regression models link a numeric outcome to input variables, helping predict values. Probability distributions describe data patterns; the normal distribution is a common first model. Bayesian methods add prior knowledge and update it with data, useful when data are small or evolving. Always check assumptions and report uncertainty, not only a single number.

Practical tips

  • Start with clean, exploratory data work. Look for missing values and unusual results.
  • Use simple models first. If a model is too complex, you may miss the message.
  • Check assumptions and validate with holdout data or cross‑validation.
  • Communicate results with clear numbers, ranges, and plain interpretation.

Example

An online store tests two page designs. Each design is shown to 200 visitors. Design A converts 10 people (5%), Design B converts 14 people (7%). The observed difference is 2 percentage points. A confidence interval or a small hypothesis test helps decide if design B truly performs better, or if the result could be due to random chance. The key idea is to quantify uncertainty so a decision can be made with care.

Key Takeaways

  • Statistics turn data into evidence and help quantify uncertainty.
  • Start simple: explore data, use straightforward models, and check assumptions.
  • Communicate results with clear numbers and honest uncertainty statements.