Why statistics matters

In data analytics, statistics helps you:

• Summarize data reliably (not just “eyeballing” charts)
• Quantify uncertainty (confidence intervals instead of single numbers)
• Compare groups fairly (hypothesis tests)
• Understand relationships (correlation vs causation)

Core vocabulary

• Population: the full set you care about
• Sample: observed subset of the population
• Parameter: a population quantity (true mean, true proportion)
• Statistic: a sample-based estimate (sample mean, sample proportion)
• Bias: systematic error (wrong sampling, leakage)
• Variance: how much an estimator varies across samples

A simple mental model

You rarely see the population. You take a sample and estimate.

• Your estimate is not exact.
• Your estimate changes if you resample.

That’s why we use:

• Distributions
• Standard error
• Confidence intervals

Common mistakes to avoid

1. Correlation ≠ causation
2. P-hacking (trying many tests until something is “significant”)
3. Ignoring base rates (rare events)
4. Selection bias (your sample isn’t representative)
5. Over-trusting averages without checking spread/outliers

Minimal Python setup

import numpy as np
import pandas as pd
import scipy.stats as stats

import numpy as np
import pandas as pd
import scipy.stats as stats

If SciPy isn’t available in your environment, you can still follow most concepts using NumPy.