Bootstrapping

Statistical Inference for Data Scientists Data science mixes numbers with uncertainty. Statistical inference helps you turn data into evidence you can trust. It answers questions like: What is the true effect size? How sure are we about the result? How likely is the observed data under a simple hypothesis? Good inferences support decisions, policies, and product choices. Understanding the core ideas Sampling variability matters. If you repeated the study many times, estimates would wobble. That is why we describe estimates with a range, not just a single number. Estimation and intervals. A point estimate gives your best guess; a confidence or credible interval shows a likely range where the true value lies. Confidence intervals. A 95% interval means that, in the long run, 95% of such intervals would cover the true value. It does not guarantee this one interval contains it. Hypothesis testing. The idea is simple: test a null idea against an alternative. A small p-value suggests the data would be unlikely if the null were true, but it does not prove anything by itself. Be careful with multiple tests and selective reporting. A practical example: an A/B test ...

Statistical Methods for Data Science Statistics is a core tool in data science. It helps turn raw numbers into understanding. This post highlights practical methods you can use in real projects, from describing data to building reliable models. You will find simple explanations and small examples you can try yourself. Foundations start with describing what you have. Descriptive statistics summarize a dataset: mean, median, mode, range, and spread. Visuals like histograms and box plots help too. For a quick demo, imagine five house prices: 200k, 250k, 275k, 300k, 350k. The average is 275k and the spread shows how far prices vary. Simple checks, like counting missing values, also guide your work. ...