Statistical methods are a practical toolset for data science. They help us describe data, test ideas, and assess how confident we should be in findings. By focusing on models, uncertainty, and evidence, these methods guide careful decision making rather than guesswork.
Core ideas A clear question: what are we trying to learn or decide? A simple model: a link between inputs and outcomes that we can estimate from data. Uncertainty: every result has a range of possible values, not a single number. Assumptions: methods rely on conditions (like distribution shape or independence) that must be checked. Communication: results should show what is known, what is uncertain, and why it matters. Common methods you will meet Descriptive statistics: summarize data with averages, spread, and patterns. Hypothesis testing: compare a claim to what the data show, using p-values to judge evidence. Regression and classification: relate inputs to outcomes, using simple or complex models. Confidence intervals: show a range where the true value is likely to lie. Bayesian methods: update beliefs as new data arrive. Resampling and cross-validation: check results on different samples to judge stability. Real-world examples A/B testing: you compare two versions to see which performs better. If the conversion rate rises from 5% to 7%, a 95% confidence interval around the difference might be [1.0%, 3.5%], suggesting real improvement rather than luck. Regression in practice: you predict house price from size and age. Coefficients tell you how much price changes with size, while diagnostic plots check linearity and constant error variance. Practical tips Treat correlation and causation as separate goals; do experiments when possible. Check assumptions before trusting a result: normality, independence, and representative samples. Plan data collection and sample size to have enough evidence. Report what you did, why you did it, and the limitations of the findings. Statistical methods stay useful when you keep them simple, transparent, and aligned with the problem you study. Use them to build trust in your data science work.
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