Why Statistical Thinking Matters

Statistics is the backbone of data science. Understanding statistical concepts helps you make better decisions and avoid pitfalls.

Core Statistical Concepts

Understanding how data is distributed helps you choose appropriate models.

Common distributions:

Normal (Gaussian) - most common

Binomial - binary outcomes

Poisson - count data

Exponential - waiting times

A structured way to test assumptions about data.

Steps:

State null and alternative hypotheses

Choose significance level (α = 0.05)

Calculate test statistic

Compare with critical value

Make a decision

Quantify uncertainty in estimates.

Example: "We're 95% confident the true mean is between 10 and 15"

The probability of observing data this extreme if the null hypothesis is true.

p < 0.05: Usually considered statistically significant

Misinterpretation is common - it's NOT the probability the null is true

**Correlation vs Causation** - Just because X and Y are correlated doesn't mean X causes Y

**Multiple Comparisons Problem** - More tests increase chance of false positives

**P-Hacking** - Testing until you get a significant result

**Ignoring Sample Size** - Small samples have high variability

**Misinterpreting P-Values** - Understanding what they actually mean

**Descriptive Statistics** - Summarizing data

**Inferential Statistics** - Making predictions about populations

**Regression Analysis** - Understanding relationships

**Time Series Analysis** - Analyzing data over time

**Bayesian Methods** - Incorporating prior knowledge

Always visualize your data

Check assumptions before using a statistical test

Report effect sizes, not just p-values

Use appropriate sample sizes

Pre-register analyses to prevent p-hacking

Replicate important findings

Books: "Statistical Rethinking" by Richard McElreath

Courses: StatQuest on YouTube

Practice: Kaggle datasets and competitions

Statistical literacy is what separates good data scientists from great ones. Invest time in understanding these concepts!