# Learn statistics — how to draw valid conclusions from data

From descriptive summaries to inferential reasoning and probability

Statistics is the science of learning from data. It provides the tools to summarize what you've observed, quantify your uncertainty, and make principled inferences about the world from limited samples. Every A/B test, clinical trial, polling result, and machine learning model validation relies on statistical reasoning.

The challenge with statistics is that it requires both mathematical precision and careful thinking about what your data actually represents. A statistically significant result can be practically meaningless. A beautiful model can be built on a biased sample. Statistics teaches you not just to calculate, but to think critically about what calculations mean.

With Planana's AI tutor, you'll build statistical intuition alongside the mathematics — learning when to apply each method and how to avoid the common traps that mislead researchers and data scientists.

## What you'll learn

### Descriptive Statistics

Mean, median, variance, standard deviation — summarizing datasets and understanding distributions visually and numerically.

### Probability

The rules of probability, conditional probability, Bayes' theorem, and common distributions (normal, binomial, Poisson).

### Statistical Inference

Using samples to make claims about populations — the core problem of statistics.

### Hypothesis Testing

Null and alternative hypotheses, p-values, significance levels, and the logic of rejecting or failing to reject a null hypothesis.

### Confidence Intervals

Quantifying uncertainty in estimates — what a 95% CI actually means and how to communicate uncertainty properly.

### Regression

Linear regression for modeling relationships, assumptions, residual analysis, and extending to multiple predictors.

### Common Pitfalls

p-hacking, Simpson's paradox, survivorship bias, confounding variables — the traps that make statistical results misleading.

## Why learn Statistics?

Data literacy has become one of the most valued skills across every industry. Product managers run A/B tests. Doctors interpret clinical trial results. Economists build forecasting models. Engineers analyze system performance data. In all of these, statistical thinking is what separates insight from noise.

For data scientists and ML engineers specifically, statistics is foundational: model evaluation, hypothesis testing, confidence intervals, and understanding bias-variance tradeoffs all require statistical fluency.

## Start learning Statistics today

Tell Planana's AI tutor your goal and get a personalized daily study plan — free.