Estimation, confidence intervals and tests · Subtopic 1 of 3
Sampling and Unbiased Estimates
We usually can't measure a whole population, so we measure a random sample and estimate. The sample mean estimates μ fairly, but for the variance we divide by n−1, not n, to stop the estimate coming out too small on average.
Distinguish a population from a sample, and a parameter from a statistic.
Explain why samples should be random, and criticise a biased method.
Calculate unbiased estimates xˉ and s2=n−11(Σx2−n(Σx)2).
Use coded totals to find unbiased estimates.
Pause and think
To estimate how many hours a week students exercise, you ask the first 20 people you meet at the gym. What is wrong with this sample?
Section 2 of 14
Core idea
Start hereTasting the soup
A chef tastes one spoonful to judge the whole pot. That works only if the soup is stirred: the spoonful must be representative. Statistics does the same with populations: study a well-chosen sample, then estimate.
The big idea
A random sample avoids bias. Unbiased estimates: xˉ for μ; s2=n−11(∑x2−n(∑x)2) for σ2 (divide by n−1, not n).
1. Sampling
A random sample of 6 from a populationRandom sampling gives every member an equal chance of being chosen, so the sample is not biased towards one type.
Population: everyone of interest. Census: measuring them all.
Random sample: every member equally likely; random numbers.
Bias: a method that systematically favours some members.
2. Why n − 1?
The sample variance underestimates
∑(x−xˉ)2≤∑(x−μ)2
xˉ is the value that minimises the sum of squared deviations from the sample.
so n1∑(x−xˉ)2 tends to be too small
It uses the sample's own centre.
E[∑(x−xˉ)2]=(n−1)σ2
(A standard result.)
s2=n−11∑(x−xˉ)2⇒E(s2)=σ2
Divide by n−1 for an unbiased estimate.
Worked exampleSample 4,7,8,9,12
Unbiased estimates of the mean and variance.
Goal
xˉ, s2.
Strategy
Sums.
1
∑x=40,∑x2=354
Totals.
2
xˉ=8
Mean.
3
s2=41(354−51600)=434=8.5
Formula.
Answer8; 8.5.
✓ CheckDeviations −4,−1,0,1,4: squares sum 34 ✓.
Pause and think
Why is surveying gym-goers poor for "average exercise of adults"?
Summary
Random samples.
xˉ; s2 with n−1.
Key words
Unbiased estimate
Correct on average.
📜 HistoryThe poll that picked the wrong president.In 1936 the Literary Digest magazine polled over two million people and predicted Alf Landon would beat Franklin Roosevelt easily. Roosevelt won in a landslide. The sample came largely from telephone directories and car registrations, which in the Depression meant wealthier voters: a huge but biased sample. George Gallup's much smaller, better-chosen sample got it right.
🤯 Fun factBessel's correction.Dividing by n−1 is called Bessel's correction, after the astronomer Friedrich Bessel. One way to see it: once you know xˉ, only n−1 of the deviations x−xˉ are free to vary (they must add to zero), so there are only n−1 "degrees of freedom".
Section 3 of 14
Key formulae
Unbiased estimate of μxˉ=nΣx
Unbiased estimate of σ²s2=n−11(Σx2−n(Σx)2)=n−1n(nΣx2−xˉ2)
With coded totalss2=n−11(Σ(x−a)2−n(Σ(x−a))2)
🪄 TrickTwo-step safety net.Work out the ordinary (divide by n) variance first, as in Paper 5, then multiply by n−1n. It uses the formula you already know and makes the "n−1" step impossible to forget.
Section 4 of 14
Worked examples
Example 1 · From totals0 / 2 steps
A sample of 8 values has Σx=96 and Σx2=1200. Find unbiased estimates of the population mean and variance.
Example 2 · Coded totals0 / 2 steps
For 20 values, Σ(x−10)=30 and Σ(x−10)2=225. Find unbiased estimates of the mean and variance.
Example 3 · Critiquing a sample0 / 1 steps
A council surveys residents' views on a new car park by asking people in the town centre on a weekday morning. Why may this be unsatisfactory?
Section 5 of 14
Explore the graph
📈How much does n − 1 change the estimate?
Loading the interactive graph…
Try this
The curve is the factor n−1n that turns the sample variance into the unbiased estimate.
For a sample of 2, the correction doubles the variance. For 50, it adds only 2%.
Why does the correction matter most for small samples?
For large samples, dividing by n or n−1 makes little difference; for small ones it matters a lot.
🌍 In the real worldTesting to destruction.Car makers crash-test a small random sample of cars, and food companies test a random sample of tins for contamination. Testing everything would destroy the whole product, so estimation from samples is not just convenient but essential.
Section 6 of 14
Common mistakes
IncorrectDividing by n for an unbiased variance
CorrectDivide by n−1
Dividing by n underestimates σ2 on average.
Incorrects2=n−1Σx2−xˉ2
Correctn−11(Σx2−nxˉ2)
The whole bracket is divided by n−1.
Incorrect"A large sample is always unbiased"
CorrectRandomness matters, not size
The Literary Digest sample was huge and biased.
IncorrectForgetting to add a back with coded data
Correctxˉ=a+nΣ(x−a)
Only the variance ignores a.
Me
the sample variance is n∑(x−xˉ)2
Unbiased estimate
divide by n−1
Me
why? Sample: because I'm always a little too close to my own mean
Section 7 of 14
Exam tip
🎯 Exam tipCriticise in context."Explain why the sample may be unsatisfactory" needs a reason linked to the situation: who is left out, and why they might be different. "It is not random" alone usually scores nothing.
Salam Academy original content · Content status: reviewed and published. Syllabus mapping: Pearson Edexcel 9FM0, First teaching 2017 (official specification).