MATH 1401 · Section 8.3 · Confidence Intervals for a Population Proportion

Confidence Intervals for a Population Proportion

Section 8.3
Case Study

Social Media Breaks for Mental Health

A 2024 Statista survey found that 35% of U.S. adults reported taking an extended break from social media because it harmed their mental health.

A university researcher wants to determine whether the proportion among college students differs from the national rate. She surveys a random sample of 300 college students and finds that 126 of them have taken such a break.

Six college students sit on a sunny lounge floor and a sofa, each looking at a phone.

Population

p=?

What we want

Sample

n=300

x=126

What we know

What can we conclude about the proportion of all college students who have taken a social media break for mental health reasons?

Point Estimate

Our "best guess" is the sample proportion:

p^=xn=126300=0.42

Point estimate

This is our point estimate: a single value used to estimate the population proportion p.

The point estimate of 0.42 is our best guess for p, but it's unlikely to be exactly correct. A more useful approach is to provide a range of possible values.

Interval Estimate

p^±some numberMargin of Error

0.42±0.100.32<p<0.52

0.42±0.050.37<p<0.47

0.42±0.010.41<p<0.43

0.300.320.340.360.380.400.420.440.460.480.500.520.540.320.520.42 ± 0.100.370.470.42 ± 0.050.410.430.42 ± 0.01p̂ = 0.42

An interval allows us to state our confidence that p falls within a range.

Confidence interval

A confidence interval is an interval that is used to estimate the value of a parameter.

Confidence Level

Begin by setting a level of confidence

Context dependent
Only "high" values make sense
Common levels are 90%, 95%, or 99%

Why not 100%? We could be 100% confident by saying p is between 0 and 1, but that tells us nothing.

Key Question

How do we determine the margin of error for a given level of confidence?

The answer lies in understanding how p^ behaves across all samples. We use the sampling distribution of p^.

Sampling Distribution

By the Central Limit Theorem for Proportions:

p^ is approximately normal
Centered at p, the population proportion
Has standard error σp^=p(1−p)n
Standard error

We don't know p, so we substitute p^ to obtain the standard error we use for the confidence interval:

Standard error =p^(1−p^)n

The Middle 95%

Suppose we want a confidence level of 95%.

Within 1.96 Standard Errors

Suppose p^ lands somewhere under this curve. Draw an interval around it that reaches 1.96·σp^ on each side.

The interval p^±1.96·σp^ contains p.

Try it

Drag p^ around the middle area.

Try it

Drag p^ out into one of the tails.

Try it

Drag p^ into the other tail.

When p^ lands in the middle area, its interval contains p.

When p^ lands in a tail, its interval misses p.

So for about 95% of all samples, the interval p^±1.96·σp^ contains p.

Margin of Error

We know: p−1.96·σp^<p^<p+1.96·σp^

Rearranging to isolate p: p^−1.96·σp^<p<p^+1.96·σp^

This is precisely in the form p^± Margin of Error

Marked: 1.96, the critical value. It depends on the confidence level.
Marked: sigma sub p-hat, the standard error, the square root of p-hat times one minus p-hat, over n.
Margin of error

Margin of Error = (Critical Value)·(Standard Error)

Margin of Error =1.96·σp^

Critical value
1.96

It depends on the confidence level.

Standard error
p^(1−p^)n

It uses p^ in place of p.

Assumptions

Constructing a confidence interval for a population proportion requires the sampling distribution to be approximately normal.

The following assumptions ensure this:

The sample is a simple random sample.
The population is at least 20 times the sample size (so sampled items are independent).
The population is divided into two categories.
The sample contains at least 10 individuals in each category.
The case study:
Simple random sample ✓
Reasonable to believe the population is at least 20 times the sample size ✓
Two categories ✓
At least 10 in each category: 126 took a break, 174 did not ✓
Recall

x=126

n=300

Case Study

Social Media Breaks for Mental Health

Point Estimate: p^=xn=126300=0.42

Confidence Level: 95%

Critical Value: 1.96

Standard Error: p^(1−p^)n=0.42(0.58)300=0.0285

Margin of Error: (Critical Value)·(Standard Error) =1.96(0.0285)=0.056

Confidence Interval: Point Estimate ± Margin of Error

0.42±0.0560.364<p<0.476

We are 95% confident that the proportion of college students who took a social media break was between 0.364 and 0.476.

Population

p=?

What we want

Sample

n=300

x=126

What we know

Confidence Interval Formula

p^±zα/2·p^(1−p^)n
Marked: z sub alpha over 2, the critical value. It depends on the confidence level.

Critical Value: depends on the confidence level

α is the area outside the middle. At 95%, α=0.05, and α/2=0.025 is the area in each tail.

The larger the confidence level, the larger the critical value.

Case Study

Social Media Breaks for Mental Health

Recall

x=126

n=300

confidence level 95%

Case Study

Social Media Breaks for Mental Health

TI-84 Plus: 1-PropZInt
The TI-84 Plus STAT TESTS menu, scrolled down to A: 1-PropZInt, which is selected.The TI-84 Plus 1-PropZInt screen with x 126, n 300 and C-Level 0.95, and Calculate.The TI-84 Plus 1-PropZInt result: the interval 0.36415 to 0.47585, p-hat 0.42, n 300.
Case Study

Social Media Breaks for Mental Health

The case study

A 2024 Statista survey found that 35% of U.S. adults reported taking an extended break from social media because it harmed their mental health.

A random sample of 300 college students found that 126 of them have taken such a break.

The 95% confidence interval, from technology

0.364<p<0.476

We are 95% confident that the proportion of college students who took a social media break was between 0.364 and 0.476.

Population

p=?

What we want

Sample

n=300

x=126

What we know

national rate 0.350.300.320.340.360.380.400.420.440.460.480.500.520.540.3640.476p̂ = 0.42

Because 0.35 does not fall within this interval, the confidence interval suggests that the proportion among college students differs from the national rate.

What does "95% confident" actually mean?

Incorrect interpretation: "There is a 95% probability that p is in this interval."

Once the interval is computed, p is either in it or it's not. There's no probability involved for any single interval.

Correct interpretation

"If we repeated this procedure many times, about 95% of the resulting intervals would contain p."

The confidence level describes how often the method succeeds, not the probability for any particular interval.

Confidence level

The confidence level is a percentage between 0% and 100% that measures the success rate of the method used to construct the confidence interval.

What does "95% confident" actually mean?

Intervals drawn
Contain p
Percent that contain p

contains pmisses pp=0.42
What "95% confident" means

In the long run, about 95% of the intervals contain p.

What we'll do: take sample after sample of size n=300 from a population where p=0.42, and build a 95% confidence interval from each one.

What to look for: the percent of intervals that contain p.

In the long run, about 95% of the intervals contain p.

  • Most intervals contain p, but now and then one misses.
  • The intervals that miss come from values of p^ in the tails.
Try it

Press Draw 1 a few times.

Watch whether each interval reaches p.

Try it

Press Draw 50.

If every interval reaches p, press Draw 50 again.

Try it

Keep pressing Draw 50.

Watch the percent that contain p.

Changing the Confidence Level

Confidence level
Intervals drawn
Contain p
Percent that contain p

contains pmisses pp=0.42
The plan

What we'll do: draw samples of size n=300 at 95%, then change the confidence level to 99%, and then to 90%. The machine keeps the same samples and redraws their intervals.

What to look for: how long the intervals are, and the percent that contain p.

More confidence means wider intervals.

  • A wider interval contains p more often, but it may be too wide to be useful.
  • Less confidence means narrower intervals, and more of them miss p.
Try it

Press Draw 50 twice at 95%.

Then click 99%.

Try it

Click 90%.

Watch the same intervals get shorter.

Changing the Sample Size

Sample size
Intervals drawn
Contain p
Percent that contain p

contains pmisses pp=0.42
The plan

What we'll do: at 95%, draw 50 samples of size n=100, then 50 samples of size n=1,000.

What to look for: how long the intervals are, and whether the percent that contain p changes.

Larger samples give narrower intervals.

  • The percent that contain p stays near 95%.
Try it

Press Draw 50 at n=100.

Then click 1,000 and press Draw 50 again.

Check your understanding

Correct Interpretation or Not?

Each statement is about the case study's 95% confidence interval, 0.364 to 0.476. Is it a correct interpretation?

(a)We are 95% confident that the proportion of all college students who took a social media break is between 0.364 and 0.476.

(a)Correct. It describes the proportion of all college students, with 95% confidence.

(b)There is a 95% probability that p is between 0.364 and 0.476.

(b)Incorrect. Once the interval is computed, p is either in it or it's not.

(c)Between 36.4% and 47.6% of the 300 students surveyed took a social media break.

(c)Incorrect. Exactly 42% of the 300 students surveyed took a break; the interval is about all college students.

(d)If we took another sample of 300, there's a 95% chance its p^ would be between 0.364 and 0.476.

(d)Incorrect. The interval is a range of values for p, not a prediction of where the next p^ will land.

Example

Example 1: Meal Kit Delivery

Among 242 households surveyed, 107 reported using a meal kit delivery service in the past year. Construct a 90% confidence interval. Industry analysts claim 43% of households have tried meal kit services. Does the confidence interval contradict this claim?

Solution

Check the assumptions:

Simple random sample ✓
Reasonable to believe the population is at least 20 times the sample size ✓
Two categories ✓
At least 10 in each category: 107 used meal kit delivery, 135 did not ✓
An open cardboard delivery box of fresh vegetables on a kitchen counter, with a salmon fillet, a steak and a blank card beside it.
Example

Example 1: Meal Kit Delivery

Construct a 90% confidence interval. Industry analysts claim 43% of households have tried meal kit services. Does the confidence interval contradict this claim?

Recall

x=107

n=242

claim 0.430.360.380.400.420.440.460.480.500.520.3900.495p̂ = 0.442

We are 90% confident that the true proportion of households that have used a meal kit service is between 0.390 and 0.495.

Because 0.43 falls within this interval, the confidence interval does not contradict the claim.

Example

Example 2: Remote Work

In a survey of 432 office workers, 108 said they prefer fully remote work over hybrid or in-person options. Construct a 99% confidence interval. Workplace consultants claim more than 32% of workers prefer fully remote. Does the confidence interval contradict this claim?

Solution

Check the assumptions:

Simple random sample ✓
Reasonable to believe the population is at least 20 times the sample size ✓
Two categories ✓
At least 10 in each category: 108 prefer remote work, 324 do not ✓
Five coworkers stand talking and laughing around a tall table by large windows, with paper cups, notepads and a potted plant.
Example

Example 2: Remote Work

Construct a 99% confidence interval. Workplace consultants claim more than 32% of workers prefer fully remote. Does the confidence interval contradict this claim?

Recall

x=108

n=432

claim: more than 0.320.160.200.240.280.320.360.400.19630.3037p̂ = 0.25

We are 99% confident that the true proportion of office workers who prefer remote work is between 0.1963 and 0.3037.

Because every value in this interval is less than 0.32, the confidence interval contradicts the claim.

Example

Example 3: Flagged Returns

An online retailer reviewed 1,000 returns and found that 6 were flagged as potentially fraudulent. Construct a 95% confidence interval. Industry analysts estimate that 2% of online returns are fraudulent. Does the confidence interval contradict this claim?

Solution

Check the assumptions:

Simple random sample ✓
Reasonable to believe the population is at least 20 times the sample size ✓
Two categories ✓
At least 10 in each category: 6 flagged as fraudulent, 994 not flagged ✗

We cannot construct a valid confidence interval using the methods of this section.

Two workers handle cardboard boxes at a counter: a woman lifts a folded sweater from an open box, and a man rests his hands on a sealed one.
Example

Example 4: AI Accelerator Chips

A data center received a shipment of AI accelerator chips. A quality control engineer tested a random sample of 200 chips and found that 24 failed to meet performance specifications. Construct a 95% confidence interval. The supplier claims that fewer than 10% of chips fail specifications. Does the confidence interval contradict this claim?

Solution

Check the assumptions:

Simple random sample ✓
Reasonable to believe the population is at least 20 times the sample size ✓
Two categories ✓
At least 10 in each category: 24 failed specifications, 176 passed ✓
A technician's gloved hand uses tweezers to hold a square processor chip above a foam mat on a lab bench, with rows of server racks blurred in the background of a data center.
Example

Example 4: AI Accelerator Chips

Construct a 95% confidence interval. The supplier claims that fewer than 10% of chips fail specifications. Does the confidence interval contradict this claim?

Recall

x=24

n=200

claim: fewer than 0.100.000.020.040.060.080.100.120.140.160.180.200.220.0750.165p̂ = 0.12

We are 95% confident that the true proportion of AI accelerator chips that fail to meet specifications is between 0.075 and 0.165.

Because this interval contains values less than 0.10, the confidence interval does not contradict the claim.

You Are Ready For:

8.3: Confidence Intervals for a Population Proportion

Find a point estimate p^ and a margin of error

Check the four assumptions

Construct a confidence interval for p, by hand or with technology

Interpret it, and decide whether it contradicts a claim

Explain why more confidence means a wider interval

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