Everything else is ready: study the parts, the key terms and the quiz below.
Why Statistics Matters for ML, AI and Real Decisions
Understand what statistics does: describe observed cases and use them carefully to reason about unobserved cases. See why model performance is an estimate about future cases, not a guaranteed score, and why the same reasoning matters outside ML.
In this lesson5 parts
- 01Statistics, forecasts and models
- 02Sample, target population and the median
- 03Absolute error and mean absolute error
- 04Estimates and statistical inference
- 05Reproducible evaluation in pandas
Key terms
The words this lesson introduces, each in one line. The module’s glossary collects them all.
- Statistics
- describing observed data and reasoning about unobserved cases.
- Descriptive statistic
- a summary of observed data. The median of the January–February trips is one.
- Statistical inference
- reasoning from observed cases about unobserved cases, such as using March trips to choose a forecast for trips that have not happened yet.
- Estimate
- a value calculated from observed cases. (00.2): the rate measured in the observed period. An estimate can change when the observed cases change.
- Unit
- one completed trip. The thing each row, and each count, is about.
- Outcome
- trip duration, the recorded quantity the forecast is trying to predict: dropoff time minus pickup time, in minutes.
- Sample
- observed recorded trips.
- Target population
- future completed trips under similar conditions; the cases a decision is meant to be used on.
- Simple model
- a rule, calculated from recorded trips, that gives a prediction for a new trip. Both forecasts in 00.1, one duration for every trip and one duration per pickup zone, are simple models.
- Median
- middle value of sorted observations. (00.2): the middle of sorted observations. With an even count, the usual median averages the two middle values.
- Evaluation
- measuring a forecast on cases excluded from its calculation.
- Absolute error
- size of observed duration minus forecast, sign ignored.
- Mean absolute error
- total absolute error divided by evaluated trips; the average size of the error per trip.
Quiz 5 questions
Your first pick on each question is the one that counts, and a right one earns a coin. Getting one wrong here is how the lesson sticks.
Practice
Problems to solve in your own notebook. Each states the problem, not the steps: working out the steps is the exercise. Level A applies the lesson, B combines it with earlier ones, C stretches it.
The self-checking notebook for this lesson is From recorded trips to a decision.
Common mistakes
What you will see when it goes wrong, why it happens, and the fix.
Where it’s used
Where this lesson’s ideas turn up in real work.