Test Billboard Campaign Conversion Rate Exceeds 60%
Quick Overview
Evaluates one-sample proportion testing for a billboard conversion rate with 65 conversions out of 100 users. Strong answers state one-sided hypotheses, compute the standard error and confidence interval, run the correct z test against 60%, and interpret why statistical evidence may be insufficient.
Test Billboard Campaign Conversion Rate Exceeds 60%
Company: TikTok
Role: Data Scientist
Category: Statistics & Math
Difficulty: easy
Interview Round: Onsite
##### Scenario
Statistical evaluation of billboard campaign: sample shows 65 % conversion on N=100 users; test if true rate exceeds 60 %.
##### Question
State the null and alternative hypotheses for this one-sided test. Compute the standard error, 95 % margin of error, and confidence interval. Based on the interval, decide whether to reject H0 at α=0.05 and interpret the result.
##### Hints
Use SE = sqrt(p(1-p)/N); margin = 1.96*SE; compare lower bound with 0.60.
Quick Answer: Evaluates one-sample proportion testing for a billboard conversion rate with 65 conversions out of 100 users. Strong answers state one-sided hypotheses, compute the standard error and confidence interval, run the correct z test against 60%, and interpret why statistical evidence may be insufficient.
Test Billboard Campaign Conversion Rate Exceeds 60%
TikTok
Jul 12, 2025, 6:59 PM
easyData ScientistOnsiteStatistics & Math
38
0
Test Whether a Billboard Campaign Conversion Rate Exceeds 60%
A billboard campaign sample contains N = 100 users, and 65 of them converted. You want to test whether the true conversion rate is greater than 60%.
Constraints & Assumptions
Treat this as a one-sample proportion test.
Assume the sample is independent and reasonably representative of the population being evaluated.
Use a one-sided test at
alpha = 0.05
.
Show both the confidence-interval intuition and the formal hypothesis-test result.
Clarifying Questions to Ask Guidance
What counts as a conversion, and over what time window?
Was the sample randomly selected from exposed users or from all eligible users?
Are repeated users or clustered observations present?
Is the 60% threshold a business target, a historical baseline, or a contractual benchmark?
Part 1 - State the Hypotheses
State the null and alternative hypotheses for the one-sided test.
What This Part Should Cover Guidance
A null hypothesis representing no evidence that the true conversion rate exceeds 60%.
An alternative hypothesis that the true conversion rate is greater than 60%.
Recognition that the test is one-sided.
Part 2 - Compute Standard Error and Confidence Interval
Compute the standard error, 95% margin of error, and 95% confidence interval for the observed conversion rate.
What This Part Should Cover Guidance
Use of
p_hat = 0.65
and
N = 100
for the Wald confidence interval calculation.
Standard error
sqrt(p_hat * (1 - p_hat) / N)
.
Margin of error
1.96 * SE
and the interval
p_hat +/- margin
.
Explanation that the two-sided CI is a useful descriptive check but the formal one-sided test uses the null standard error.
Part 3 - Make the Test Decision
Using a one-sided z test, decide whether to reject the null at alpha = 0.05 and interpret the result.
What This Part Should Cover Guidance
Test statistic based on
p0 = 0.60
:
(p_hat - p0) / sqrt(p0 * (1 - p0) / N)
.
One-sided p-value and comparison with
0.05
.
Conclusion that distinguishes "not enough evidence" from "the conversion rate is definitely not above 60%."
What a Strong Answer Covers Guidance
A strong answer states the hypotheses clearly, computes the interval correctly, uses the proper one-sided test statistic for the decision, and explains the practical meaning of failing or succeeding to reject the null.
Follow-up Questions Guidance
How would the conclusion change if the sample size were 1,000 with the same observed rate?
When would you prefer an exact binomial test over a normal approximation?
What additional validity checks would you run before trusting the result?