1. Hello Data

b. Population and Sample

Yurk

Where We Left Off

In 1a we met loan50 — a sample of 50 loans from Lending Club — and learned to classify variables as numerical or categorical.

In 1b we’ll see why we care about the difference between the sample we have (loan50) and the population it came from (every Lending Club loan).

Population vs. Sample

Population: The complete group we want to study

  • All loans ever made through Lending Club

Sample: A subset of the population we actually observe

  • Our 50 loans in the loan50 dataset

Parameter vs. Statistic

Parameter: A numerical value describing the population (what we want to know)

  • The true mean loan amount for ALL Lending Club loans
  • Usually unknown — we can’t measure it

Statistic: A numerical value calculated from our sample (what we can measure)

  • The mean loan amount in our 50 loans
  • Serves as our estimate of the parameter

Putting It Together

How the pieces fit together:

Identifying Parameters and Statistics

Context: we study 50 loans sampled from Lending Club’s millions of loans.

Classify each value as a parameter or statistic:

Value Type
Mean loan amount in loan50 = $17,083.00 statistic
True mean loan amount for all Lending Club loans parameter
Proportion of loan50 with credit grade A statistic
True proportion of all Lending Club loans with grade A parameter
Mean height in a sample of 30 Hope students statistic
Mean height of all Hope students parameter

Caution: Anecdotal Evidence

An anecdote is evidence from a single case or personal story.

Claims you’ll hear:

  • “My friend got a 25% interest rate”
  • “Everyone I know prefers X”

Why they mislead:

  • One case doesn’t represent a whole population
  • Memorable cases are often the unusual ones — not a representative sample

The fix: a representative sample, then careful reasoning from sample back to population (called statistical inference) — the main work of this course.

References

Course Home