Intro to Statistics: From "Looking at Data" to "Interpreting Data"

What you're probably confused about right now

"A high average means good performance, right?"

Not necessarily. You also need to look at dispersion and sample size.

One-line definition

Statistical analysis uses mean, median, variance, and standard deviation to describe data distribution and support decision-making.

Real-life analogy

Two classes both average 80 on a test. But one is tightly grouped, the other is all over the place. Very different conclusions.

Minimal working example

import statistics
data = [80, 90, 78, 92, 88]
print(statistics.mean(data))
print(statistics.median(data))
print(statistics.stdev(data))

Quick quiz (5 min)

1. Calculate mean and standard deviation for two datasets.
2. Compare which group is more stable.
3. Write a brief conclusion.

Quiz answer guide & grading criteria

• Answer direction: write runnable code that covers the core requirements and edge cases from the prompt.
• Criterion 1 (Correctness): Main flow produces correct results, key branches execute.
• Criterion 2 (Readability): Clear variable names, no excessive nesting.
• Criterion 3 (Robustness): Basic protection against null values, type errors, or unexpected input.

Take-home task

Implement analyze_scores(scores) that returns a structured statistics result.

Acceptance criteria

You can independently:

• Calculate core statistical metrics
• Explain what each metric means
• Avoid drawing conclusions from a single metric

Common errors & debugging steps (beginner edition)

• Can't read the error: start from the last line -- find the error type (TypeError, NameError, etc.), then trace back to the line in your code.
• Not sure about a variable's value: throw in a temporary print(var, type(var)) at key points to verify data looks right.
• Changed code but nothing happened: make sure the file is saved, you're running the right file, and your terminal is in the correct venv.

Common misconceptions

• Misconception: just look at the mean and you're good.
• Reality: combine variance/standard deviation with sample size.