Every election season, pollsters publish results like this: “Candidate A leads with 54% support — margin of error ±3 percentage points, 19 times out of 20.” That last phrase — “19 times out of 20” — is not a throwaway disclaimer. It is the heart of a 95% confidence interval. But where does the ±3 come from? How do pollsters know what sample size they need to hit that precision? And what exactly are they claiming when they publish an interval?
In INF-2, you built confidence intervals for a population mean using the formula . This lesson keeps the same skeleton — point estimate ± margin of error — but swaps in a new statistic: the sample proportion. The central challenge is that proportions have their own standard error formula, and that formula introduces a subtlety that trips up almost everyone the first time.
Here is the real-world question we will be able to answer by the end of this lesson: A Léger Research poll contacts 1,000 Canadians and finds that 540 support a proposed climate policy. How confident can we be in the true level of support across all Canadians, and how many people would we need to survey to narrow that uncertainty further?
After this lesson, you will be able to:
Compute the sample proportion and identify it as a point estimate for the population proportion
Verify the conditions required for the z-interval to be valid ( and )
Construct a two-sided confidence interval for using
Interpret a confidence interval correctly — in terms of the method’s long-run reliability, not the probability that falls in any single interval
Determine the minimum sample size needed to achieve a target margin of error, both with and without a prior estimate of
If INF-2 gave you the blueprint, this lesson gives you a new set of tools for a different kind of question. The math is closely parallel — which means most of what you already know transfers directly.
Section 2: Prerequisites
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Confidence intervals for proportions are built on the same “Point Estimate ± Margin of Error” framework you mastered in INF-2.
From INF-2: Critical Z-Values. The same values ( for 95%) apply whenever we use the normal distribution as our model.
From INF-2: The Quadruple Rule. To cut the margin of error in half, you must multiply the sample size by four.
Percentages to Proportions: is the same as . You must use the decimal form in all formulas.
Square Root Properties:. You will be working with , so ensure you are taking the square root of the entire result.
Retrieval Checkpoint
A poll reports a 95% confidence interval for a proportion as . What is the margin of error () in this study?
Success Factor:
In this lesson, we use only if the sample size is “large enough.” If or , the normal distribution is a poor model and these methods cannot be used. Always check your conditions first.
Retrieval Warm-up — from earlier lessons
A study reports , , . What is the margin of error for a 95% confidence interval for the population mean?
You read a news article: “A new 95% CI shows the government approval rating is between 42% and 50%.” A researcher says this is the sample size determination problem — she wants to cut the margin of error in half before the next election. By what factor must she multiply the sample size?
Section 3: Core Concepts
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Navigation tip: Seven concepts live in this section. They build on each other in order — C1 through C4 give you the interval formula, C5 tells you when to use it, C6 tells you how to talk about it, and C7 adds the sample-size tool. If you already know INF-2 well, C1–C4 will go quickly.
C1 — The Sample Proportion
Suppose we want to know what fraction of Montréal adults have a library card. We can’t ask all 1.8 million — so we sample people and count how many () have one. The sample proportion is simply that fraction:
Sample Proportion
If individuals in a random sample of size have a characteristic of interest, the sample proportion is:
(read “p-hat”) estimates the unknown population proportion. must be a count (a whole number); is the sample size.
Notice the notation carefully: is the true proportion in the whole population — unknown, fixed, a parameter. is what we compute from our data — known, varies sample to sample, a statistic. This distinction is going to matter a great deal when we write the standard error formula.
and are not the same thing. is the true population proportion — it exists, but we don’t know its value (that’s why we’re building an interval). is our best guess from the data. We will always use in our calculations, never — because we don’t have .
The visualization below makes this concrete: stays fixed (and hidden — you don’t get to know it) while each new sample produces a different . Watch the values scatter around the one fixed .
Figure: The dashed line is the true proportion p — one fixed value that never moves (and
is usually unknown, so it starts hidden). Each dot is a different sample's p̂ = x/n.
Draw repeatedly: the dots scatter and cluster around the line. p̂ changes every sample;
p does not.
C2 — The Standard Error of
In INF-2, the standard error of was . Where did that come from? From the variance of a sum of random variables. The same idea applies here — but for a proportion.
Think of each sampled person as a Bernoulli trial: success (has the characteristic) with probability , failure with probability . The sample count is binomial. Recall from PR-4 that a binomial random variable has variance . Dividing by to get the proportion gives variance . Taking the square root gives the standard error:
Standard Error of a Proportion
The theoretical standard error of is:
Since is unknown, we substitute to get the estimated standard error:
Notice: we estimate an estimate. We use to estimate for the SE formula itself. This works fine in practice, but it’s one more reason the conditions check (C5) matters — the approximation is only reliable when is large enough.
Never write in a calculation. You do not know . The formula that goes into actual computations always uses : . Only the theoretical expression uses .
C3 — Margin of Error
The margin of error is the ”±” part of the interval. It answers: “How far from do we need to reach to be confident we’ve captured ?”
Margin of Error for a Proportion
where is the critical value for the desired confidence level (1.645 for 90%, 1.96 for 95%, 2.576 for 99%).
The margin of error is the half-width of the interval — not the full width. A 95% CI with spans 0.06 units in total. When a poll reports “margin of error ±3 points,” that ±3 is exactly .
The margin of error is half the interval width. If , the interval is , which has total width 0.08, not 0.04. The ”±” already signals this — but it’s easy to report as the width when writing up results.
C4 — The Confidence Interval Formula
Two-Sided Confidence Interval for a Proportion
A confidence interval for the population proportion is:
which gives the interval .
Conditions must be checked before applying this formula (see C5).
The structure is identical to INF-2: point estimate ± (critical value × standard error). Only the point estimate and SE formula have changed. Everything else — the z* values, the interpretation logic, the sample size approach — carries over directly.
Figure: Drag the sliders to see how n and p̂ affect the interval width. Switch confidence
levels to see the width–confidence tradeoff. The row at the bottom always shows what happens when
you quadruple the sample size: E is halved.
C5 — Conditions for the z-Interval
The CI formula works because is approximately normally distributed when the sample is large enough (this follows from the CLT applied to a proportion). “Large enough” has a specific meaning here:
Conditions for the z-Interval for a Proportion
Before computing a CI for , verify both:
— at least 10 successes in the sample
— at least 10 failures in the sample
Also assume the data come from a random sample (or can be treated as one), and that of the population size (so observations are approximately independent).
Intuitively: if almost everyone in your sample is a “success” (say, with ), the distribution of is strongly skewed — the normal approximation breaks down. The conditions and ensure there are enough of both outcomes for the CLT to kick in symmetrically. Requiring at least 10 of each (rather than 5) provides a meaningful buffer against skewness.
Figure: Exact binomial probabilities for p̂ (bars) vs. the normal approximation (curve). When n·p or n·(1−p) falls below 10, the bars are visibly asymmetric and
the curve is a poor fit — the conditions badges flip red. Increase n or move p toward
0.5 to watch them flip green.
Check conditions with , not . You’re checking whether your observed sample has enough successes and failures. Use and — these are literally the observed count of successes and failures in your sample (i.e., and ).
C6 — Correct Interpretation
This is the concept most students get wrong — even after getting the arithmetic right. The issue is subtle but important.
Imagine running 1,000 polls, each on a fresh random sample of the same size. Each poll gives a different , and therefore a different interval. A 95% CI procedure guarantees that 950 of those 1,000 intervals will contain the true . The other 50 will miss.
Now you run your poll. You get one specific interval — say, (0.48, 0.56). Either is in that interval or it isn’t. There is no randomness left: is a fixed (unknown) number, and your interval is a fixed pair of numbers.
Figure: Each bar is a confidence interval built from a different random sample of size n. Teal bars capture the fixed true p (dashed line); red bars miss it. The line never moves
— only the intervals vary. Over many repetitions, approximately C% of all such
intervals will contain p.
Correct CI Interpretation
“We are 95% confident that the true population proportion lies between [lower] and [upper].”
This means: the method we used to build this interval captures the true in 95% of all possible samples of this size.
“There is a 95% probability that is between [lower] and [upper].” This statement is incorrect. is not random — it is a fixed population parameter. It doesn’t have a probability of being in a range; it either is or isn’t. The 95% refers to the procedure, not to the probability that is in any one specific interval.
C7 — Sample Size Determination
Suppose you want to design a poll. You want your margin of error to be at most at a given confidence level. How large does your sample need to be? Solve the margin-of-error formula for :
Sample Size Formula for a Proportion
To achieve a margin of error of at most :
where is your best prior estimate for . If no prior estimate is available, use — this maximizes and gives the largest (most conservative) sample size.
Always round up to the next whole number.
Why does give the largest sample? Because is maximized at (you can verify: , while and ). Using ensures you won’t undershoot the required sample size, no matter what the true turns out to be.
Figure: The curve shows p*(1−p*) — the factor that drives sample size — as a function of
the prior estimate p*. The teal dot at p* = 0.5 marks the peak (0.25): choosing p* = 0.5
guarantees the largest required n, covering all possible true p. The red dot shows your
current p*. The shaded zone shows every true p value that would require more samples than
your current choice provides — drag the slider toward 0.5 to watch the zone disappear.
Always round sample size UP. If the formula gives 384.16, you need 385 people — not 384. Rounding down means you’ve committed to a margin of error larger than the target, which defeats the purpose of the calculation.
Looking ahead: In inf-6, you will use the proportion — a specific hypothesized value — to test a claim. The notation (observed) vs. (hypothesized) will become critical. Start noticing this distinction now.
Section 4: Worked Examples
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Three examples with progressively less scaffolding. Work through each one at your own pace — the first is fully narrated so you can see the complete thought process.
Example 1 — A Transit Policy Poll (Fully Worked)
Problem: A Léger poll contacts 600 Quebecers at random. Of those, 312 say they support a proposed new transit line. Build a 95% confidence interval for the true proportion of Quebec residents who support the policy.
Step 1: Identify what we know.
(sample size)
(number of supporters in the sample)
Confidence level = 95%, so
Step 2: Compute the sample proportion.
So 52% of our sample supports the policy. This is our point estimate for .
Step 3: Check the conditions.
We need and :
✓
✓
Conditions are met. (Notice: and — the raw counts themselves.)
Step 4: Compute the standard error.
Step 5: Compute the margin of error.
Step 6: Build the interval.
Lower bound: Upper bound:
Step 7: Interpret in context.
We are 95% confident that the true proportion of Quebec residents who support the transit policy is between 48.0% and 56.0%.
Reality check: The margin of error is ±4.0 percentage points, consistent with what you’d see in a real Léger poll of this size. A sample of 600 gives reasonably precise estimates for proportions near 0.5 — but the interval still spans 8 full percentage points. To cut that in half, you’d need to quadruple the sample size.
Example 2 — Quality Control (Partially Scaffolded)
Problem: A quality-control inspector randomly samples 80 juice bottles from a production run. She finds 12 with a filling defect. Build a 90% confidence interval for the true defect rate.
Setup:, , confidence level = 90%.
The defect rate in our sample is . At 90% confidence, we use . Do you expect the 90% interval to be wider or narrower than a 95% interval on the same data?
Step 1: Sample proportion.
Step 2: Check conditions.
✓
✓
Step 3: Standard error and margin of error.
Step 4: Interval.
Interpretation: We are 90% confident that between 8.4% and 21.6% of all bottles produced in this run have a defect.
Answer to the prediction: Narrower. Lower confidence → smaller → smaller . The 95% interval on the same data would be — noticeably wider.
Example 3 — Designing a CEGEP Survey (Minimally Scaffolded)
Problem: A researcher wants to estimate the proportion of CEGEP students in Quebec who work more than 15 hours per week during the semester. She wants a margin of error of at most 4 percentage points at 95% confidence. No prior estimate of the proportion is available. How many students must be surveyed?
Hint: Use the sample size formula with (worst case), , and . Solve for and round up.
Show Solution
Round up: students.
Interpretation: To guarantee a margin of error of at most 4 percentage points at 95% confidence — regardless of the true proportion — we must survey at least 601 students.
Common mistake: Rounding 600.25 down to 600. Always round up — 601 guarantees the target precision; 600 does not.
Section 5: Guided Practice
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Build the procedure in order first; then choose the method at the decision points where an answer cannot be produced by calculation alone.
Problem 1 — Sample Proportion and Condition Verification (C1)
Problem 2 — Standard Error and Interval Construction (C2–C4)
Problem 3 — Practical Interpretation of Confidence (C6)
A researcher surveys 500 adults and finds that 310 support stricter food labelling laws. The resulting 95% CI is (0.577, 0.663).
Which of the following is the correct interpretation?
Problem 4 — Determining Required Sample Size (C7)
Section 6: Independent Practice
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These are cold retrieval problems: choose the quantity to produce, commit an answer, then use the feedback to diagnose your method.
Problem 1 — Build a Confidence Interval (C1–C4)
Problem 2 — Margin of Error and Interpretation (C3, C6)
After you check your numerical result, state the interval’s interpretation in your own words: the confidence level describes the method’s long-run capture rate, not the probability of a fixed population proportion.
Problem 3 — Determine the Required Sample Size (C7)
Problem 4 — When the Conditions Are Not Met (C5)
Mixed Review — Retrieval from Earlier Lessons
Section 7: Mastery Check
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No hints. No guided steps. These questions measure whether the core ideas have actually landed. Take your time with each one — especially the Feynman test.
Question 1 — Feynman Test
A friend who missed this lesson asks you: “The formula for the standard error of a proportion involves — but you said is what we’re trying to estimate. So aren’t we using an unknown to find an unknown? Doesn’t that break the whole thing?”
Explain your answer below in plain language, as if talking to your friend. Address both why we use and what limits that introduces.
0 / 1000
Show a model answer
You’re right that we don’t know — that’s exactly why we’re building the interval. The SE formula theoretically requires , but since is unknown, we substitute our best estimate: . This works because when the sample is large enough, is close to , so the estimated SE is close to the true SE.
The catch: this substitution introduces extra uncertainty — especially when is far from (which tends to happen with small samples or extreme proportions). That’s exactly why we check the conditions and first. When those hold, the approximation is good enough for practical purposes. When they don’t, the interval can be meaningfully misleading, and we need a different method.
Question 2 — Vegetable Intake CI
The National Institute of Nutrition surveys 450 adults and finds that 180 consume fewer than two servings of vegetables per day. They want to estimate the true proportion with 99% confidence.
(a) Which formula should be used for the standard error?
(b) Verify conditions, then compute the 99% CI.
Question 3 — Error Analysis
Diagnose each statement before reading its feedback. Together, the two items name the two interpretation errors that often travel together.
Question 4 — Sample-Size Retrieval (C7)
Self-Assessment
How confident are you with the concepts from this lesson?
Still confusedReady for the Boss Fight
If your confidence is below 60%, revisit Section 3 and re-do the worked examples before the Boss Fight. The next scenario expects you to decide what the data can support.
Section 8: Boss Fight
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Transit Service Decision Brief
A city is deciding how to report and plan around a late-night transit service. Work through one coordinated decision brief: first establish whether the survey can support a population statement, then build the range, assess a claim, and price the next round of evidence. The prompts name no method; decide what the situation requires.
Evidence check
What the current survey supports
Plan the next survey
Reflection: Draft a two-sentence recommendation for the transit team. Separate what its current evidence can support from what it would need to learn next.
0 / 600
Section 9: Challenge Problems
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Optional stretch problems — these go beyond the lesson objectives. They’re here for students who want to push further. C1 previews a more robust method; C2 builds deep intuition; C3 requires creative multi-step reasoning.
Challenge 1 — The Wilson Score Interval
The standard z-interval can fail near 0 or 1 or with a small sample. This optional extension compares it with the Wilson score interval; its solution remains deliberately ungraded because Wilson intervals are outside this lesson’s core outcomes.
Challenge 2 — The Shape of the Margin of Error
For a fixed and confidence level of 95%, the margin of error is:
This is a function of .
(a) Compute for .
(b) At what value of is maximized? Why does this make sense?
(c) A researcher claims: “I used in the sample size formula, so I’m safe for any .” Is this correct? Explain.
Show Solution
(a) Using :
0.1
0.09
0.0294
0.2
0.16
0.0392
0.3
0.21
0.0449
0.4
0.24
0.0480
0.5
0.25
0.0490
0.6
0.24
0.0480
0.7
0.21
0.0449
0.8
0.16
0.0392
0.9
0.09
0.0294
(b) is maximized at , where . This makes sense: is maximized at 0.5 because that’s where a Bernoulli random variable has maximum variance. A proportion near 0.5 means successes and failures are equally unpredictable — maximum uncertainty.
(c) Incorrect. Using gives , which is not the worst case. The worst case is always (giving 0.25). For between 0.5 and 0.7, the product ranges from 0.21 to 0.25 — using would underestimate the required sample size for any true in that range. Safe means conservative: use unless you’re very confident the true proportion is far from 0.5.
Challenge 3 — Two Polls, One Question
Section 10: Solutions Reference
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Complete, step-by-step solutions for all problems in Sections 5–9 are available on the solutions page. Solutions include worked arithmetic, common mistakes to watch for, and interpretation guidance.
If you’re stuck: Re-read the relevant Core Concept in Section 3, then find the Worked Example that maps to that concept (e.g., Example 1 maps to Concept 1). The solutions page shows the reasoning behind every step, not just the final answer.
Quick-Reference Formulas
Sample Proportion:
Standard Error of :
Confidence Interval for :
Required Sample Size:(Use for a conservative estimate if no prior estimate is available. Always round up to the next whole number)