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Teacher Guide: Sampling Distributions

Learn how sample means and sample proportions vary from sample to sample, what the standard error is, and why the central limit theorem makes them approximately normal.

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Class quiz

10 questions on Probability Distributions. Students join with a name, you see everyone's score.

For Teachers

Learning Objectives
  • Distinguish a parameter from a statistic and use the symbols μ\mu, σ\sigma, pp, xˉ\bar{x}, p^\hat{p} correctly
  • Find the mean and standard error of the sampling distribution of xˉ\bar{x} and of p^\hat{p}
  • State the central limit theorem and decide when a sampling distribution is approximately normal
  • Compute probabilities about a sample mean or a sample proportion with z-scores and the z-table
  • Explain why larger samples give more precise estimates and find the sample size for a given standard error
Prerequisites
  • • Introduction to Normal Distribution: mean, standard deviation and the bell curve
  • • Z-Scores: z=x−μσz = \dfrac{x - \mu}{\sigma}
  • • Standard Normal Distribution: reading the z-table
  • • Normal Probability: areas to the left, right and between values
  • • Calculating Standard Deviation: the spread of a data set
Discussion Starters
  • 1. Two polls of the same election give 48% and 52%. Does that mean one of them made a mistake?
  • 2. Why is it much harder for the average of 36 batteries to be below 47 hours than for one battery?
  • 3. A company says it surveyed 10 customers and 90% were happy. What would you want to know before trusting that number?
  • 4. Why does doubling the sample size not halve the standard error?
Common Misconceptions

A large sample makes the data normal

Remediation: In the simulator, compare the histogram of ONE big sample (it looks like the skewed population) with the histogram of many sample MEANS (it becomes a bell curve).

The standard error and the standard deviation are the same thing

Remediation: Show the population panel (σ\sigma) next to the SD of the sample means in the simulator. Ask students to predict the second one with σ/n\sigma / \sqrt{n} before pressing the button.

A bigger population needs a bigger sample

Remediation: Show that the formula σ/n\sigma / \sqrt{n} has no population size in it. Compare a soup tasting analogy: one spoonful tells you the taste of a small pot or a big pot equally well, if it is stirred.

Differentiation Ideas

For Struggling Students:

  • • Start with the die simulator and only the center: the averages always pile up around 3.5
  • • Give a table with nn, n\sqrt{n} and σ/n\sigma / \sqrt{n} for perfect squares (4, 9, 16, 25, 36, 100)
  • • Practice sorting numbers into parameter and statistic before any formula

For On-Level Students:

  • • Compute standard errors and z-scores for sample means with the z-table
  • • Check the conditions for the normal model of p^\hat{p} and compute its standard error
  • • Compare the probability for one value with the probability for a sample mean

For Advanced Students:

  • • Find the sample size needed for a given standard error or margin of error
  • • Explore the skewed and bimodal populations to see how fast the central limit theorem works
  • • Derive σ/n\sigma / \sqrt{n} from the rule that variances of independent values add
Standards Alignment
  • HSS-IC.A.1 (CCSS.MATH.CONTENT.HSS.IC.A.1)

    Understand statistics as a process for making inferences about population parameters based on a random sample from that population

  • HSS-IC.B.4 (CCSS.MATH.CONTENT.HSS.IC.B.4)

    Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling

  • HSS-ID.A.4 (CCSS.MATH.CONTENT.HSS.ID.A.4)

    Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages

Lesson Resources
  • visualSampling Simulator

    Draw 1, 100 or 1000 samples from a skewed population, a die or a yes/no population and watch the histogram of sample means become normal

  • activityClass Dice Experiment

    Each student rolls 5 dice and reports the mean; the class builds a dot plot of the means and compares it with single rolls

  • worksheetSample Mean and Proportion Probabilities

    Practice standard errors, condition checks and z-table probabilities for sample means and proportions

Lesson Content

Everything students see: definition, examples, common mistakes, applications. Tap to open.

Definition

A parameter is a number that describes a whole population, for example the population mean μ\mu, the population standard deviation σ\sigma or the population proportion pp. A statistic is a number computed from a sample, for example the sample mean xˉ\bar{x} or the sample proportion p^\hat{p}. The parameter is fixed (but usually unknown); the statistic changes from sample to sample.
The sampling distribution of a statistic is the distribution of its values over all possible random samples of the same size nn.

Sampling distribution of the sample mean xˉ\bar{x}

  • Center: μxˉ=μ\mu_{\bar{x}} = \mu (sample means are centered on the population mean)
  • Spread: σxˉ=σn\sigma_{\bar{x}} = \dfrac{\sigma}{\sqrt{n}}, called the standard error
  • Shape (central limit theorem): if the population is normal, xˉ\bar{x} is normal for any nn. If not, xˉ\bar{x} is still approximately normal when nn is large, usually n≥30n \geq 30.
So a probability about a sample mean uses the z-score
z=xˉ−μσ/nz = \frac{\bar{x} - \mu}{\sigma / \sqrt{n}}

Sampling distribution of the sample proportion p^\hat{p}

  • Center: μp^=p\mu_{\hat{p}} = p
  • Spread: σp^=p(1−p)n\sigma_{\hat{p}} = \sqrt{\dfrac{p(1-p)}{n}}
  • Shape: approximately normal when np≥10np \geq 10 and n(1−p)≥10n(1-p) \geq 10
These formulas assume a random sample of independent values (in practice: the sample is less than about 10% of the population).

Worked Examples

A school has 1200 students. Their mean height is 165 cm. A class project measures a random sample of 50 students and finds a mean height of 163.2 cm. Which number is a parameter and which is a statistic?
1

Find the number that describes the whole population

165 cm is the mean of all 1200 students → Parameter: μ=165\mu = 165 cm
2

Find the number that comes from the sample

163.2 cm is the mean of the 50 measured students → Statistic: xˉ=163.2\bar{x} = 163.2 cm
3

Think about a second sample

Another random sample of 50 students would give a slightly different mean, for example 166.0 cm → xˉ\bar{x} varies, μ\mu stays fixed

Common Mistakes

Using σ\sigma instead of σ/n\sigma / \sqrt{n} in a question about a sample mean

Why it's wrong: σ\sigma is the spread of single values. Sample means vary much less, so using σ\sigma makes unusual averages look far too likely.

Correct: For xˉ\bar{x} always divide by the standard error: z=xˉ−μσ/nz = \dfrac{\bar{x} - \mu}{\sigma / \sqrt{n}}. For one value use z=x−μσz = \dfrac{x - \mu}{\sigma}.

Dividing by nn instead of n\sqrt{n}

Why it's wrong: With σ=12\sigma = 12 and n=36n = 36, dividing by nn gives 0.330.33 instead of the correct standard error 22, so every z-score comes out 6 times too large.

Correct: Take the square root of nn first: 1236=126=2\dfrac{12}{\sqrt{36}} = \dfrac{12}{6} = 2.

Thinking the central limit theorem makes the population or the data normal

Why it's wrong: A large sample from a skewed population is still skewed: its histogram looks like the population. Only the distribution of the sample MEANS becomes normal.

Correct: The central limit theorem is about the sampling distribution of xˉ\bar{x}, not about individual values.

Using the normal model for p^\hat{p} without checking np≥10np \geq 10 and n(1−p)≥10n(1-p) \geq 10

Why it's wrong: With p=0.05p = 0.05 and n=40n = 40 we get np=2np = 2: most samples have 0, 1, 2 or 3 successes, and the distribution of p^\hat{p} is strongly skewed.

Correct: Check both conditions first. If one fails, take a bigger sample or use the binomial distribution.

Why It Matters

Almost every number in the news that comes from a survey is a statistic, not a parameter:
  • Polls: a poll of 1000 voters estimates the share of all voters. The standard error tells you how far the poll is likely to be from the truth.
  • Quality control: a factory checks the mean weight of a sample of packets instead of weighing every packet.
  • Medicine: a clinical trial measures the mean effect on a few hundred patients and draws conclusions about everyone.
  • Science: averaging repeated measurements reduces random error.
The key idea: the standard error σ/n\sigma / \sqrt{n} shrinks as the sample grows. Four times as many people halve the error, which is why larger polls are more precise. The central limit theorem then lets you use the normal distribution, z-scores and the z-table you already know, even when the population itself is not normal.

Real World Applications

Election Polls

Polling companies ask about 1000 people and report a margin of error. That margin comes straight from the standard error of $\hat{p}$.

Example:

If the true support is p=0.5p = 0.5, a poll of 1000 people has standard error 0.5⋅0.5/1000≈0.016\sqrt{0.5 \cdot 0.5 / 1000} \approx 0.016, so about 95% of such polls land within roughly 3 percentage points of the truth.

1Try It Yourself

A smaller poll asks n=400n = 400 randomly chosen voters. The true support for a candidate is p=0.5p = 0.5.

What is the standard error of the sample proportion p^\hat{p}?

Step 1: Write the mathematical expression

Compute p(1−p)n\sqrt{\dfrac{p(1-p)}{n}}:

Quality Control in a Coffee Shop

Machines that fill cups are checked by averaging small samples. An average that is too far from the target, measured in standard errors, sets off an alarm.

Example:

A machine fills cups with μ=250\mu = 250 mL and σ=8\sigma = 8 mL. An inspector measures 16 cups, so the standard error is 8/16=28 / \sqrt{16} = 2 mL.

2Try It Yourself

The 16 cups have a mean of xˉ=246\bar{x} = 246 mL. The machine is set to μ=250\mu = 250 mL with σ=8\sigma = 8 mL.

What is the z-score of this sample mean?

Step 1: Write the mathematical expression

Compute z=xˉ−μσ/nz = \dfrac{\bar{x} - \mu}{\sigma / \sqrt{n}}:

Planning a Medical Study

Before a study starts, researchers choose the sample size so that the standard error is small enough. Because of the square root, halving the error needs four times as many patients.

Example:

Blood pressure has σ=20\sigma = 20 mmHg. With 25 patients the standard error is 20/5=420 / 5 = 4 mmHg; with 100 patients it is 20/10=220 / 10 = 2 mmHg.

3Try It Yourself

Researchers want the standard error of the mean blood pressure to be 2 mmHg. The population standard deviation is σ=20\sigma = 20 mmHg.

How many patients do they need?

Step 1: Write the mathematical expression

From σn=2\dfrac{\sigma}{\sqrt{n}} = 2 we get n=(σ2)2n = \left(\dfrac{\sigma}{2}\right)^2:

Key Takeaways

  • 1A parameter (μ\mu, σ\sigma, pp) describes the population and is fixed; a statistic (xˉ\bar{x}, p^\hat{p}) comes from a sample and varies from sample to sample
  • 2The sampling distribution of xˉ\bar{x} has mean μ\mu and standard error σ/n\sigma / \sqrt{n}
  • 3Central limit theorem: for n≥30n \geq 30 (about) the sample mean is approximately normal whatever the population shape; if the population is normal, for any nn
  • 4For a probability about a sample mean use z=xˉ−μσ/nz = \dfrac{\bar{x} - \mu}{\sigma / \sqrt{n}} and the z-table
  • 5The sample proportion p^\hat{p} has mean pp and standard error p(1−p)/n\sqrt{p(1-p)/n}; it is approximately normal when np≥10np \geq 10 and n(1−p)≥10n(1-p) \geq 10
  • 6Bigger samples give more precise estimates: four times the sample size halves the standard error

Frequently Asked Questions

Why does the standard error use n\sqrt{n} and not nn?

The variance of a mean of nn independent values is σ2/n\sigma^2 / n. The standard error is its square root: σ2/n=σ/n\sqrt{\sigma^2 / n} = \sigma / \sqrt{n}. That is why you need four times as many values to halve the error.

Is 30 a magic number?

No, it is a rule of thumb. For a population that is already close to normal, much smaller samples are fine; for a very skewed population (like incomes) you may need more than 30. Try it in the simulator: the skewed population needs a bigger nn than the die.

What is the difference between standard deviation and standard error?

The standard deviation σ\sigma measures how much single values vary. The standard error σ/n\sigma / \sqrt{n} measures how much a statistic, like the sample mean, varies from sample to sample. The standard error is always smaller when n>1n > 1.

Does the size of the population matter?

Hardly, as long as the sample is a small part of the population (less than about 10%). A random sample of 1000 people is about equally precise for a city or for a whole country. What matters is the sample size nn.

Glossary

Parameter
A number that describes a whole population, such as μ\mu, σ\sigma or pp; usually unknown
Statistic
A number computed from a sample, such as xˉ\bar{x} or p^\hat{p}; it changes from sample to sample
Sampling distribution
The distribution of a statistic over all possible random samples of the same size nn
Standard error
The standard deviation of a sampling distribution, for example σ/n\sigma / \sqrt{n} for the sample mean
Central limit theorem
For large nn the sampling distribution of the sample mean is approximately normal, whatever the shape of the population
Sample proportion
The share of successes in a sample, p^=number of successesn\hat{p} = \dfrac{\text{number of successes}}{n}

Formula Card

Mean of the sample mean

μxˉ=μ\mu_{\bar{x}} = \mu

Sample means are centered on the population mean

Standard error of the mean

σxˉ=σn\sigma_{\bar{x}} = \dfrac{\sigma}{\sqrt{n}}

Spread of the sample means; shrinks as $n$ grows

z-score of a sample mean

z=xˉ−μσ/nz = \dfrac{\bar{x} - \mu}{\sigma / \sqrt{n}}

Use with the z-table when $n \geq 30$ or the population is normal

Standard error of a proportion

σp^=p(1−p)n\sigma_{\hat{p}} = \sqrt{\dfrac{p(1-p)}{n}}

Normal model if $np \geq 10$ and $n(1-p) \geq 10$

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