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Correlation Claim in Marketing Spend | Dataford Interview Questions - Dataford - Ace your Interview

Correlation Claim in Marketing Spend

Easy

Statistics & Probability

Asked at 7 companies7Hypothesis TestingStatistical SignificanceCausal Inference

Also asked at

Problem

Business Context

BrightAds, a mobile commerce company, reviewed recent campaign data and concluded that increasing daily ad spend causes higher same-day revenue because the two variables move together strongly.

Problem Statement

You are given 10 days of campaign data. The analyst reported a high positive correlation between ad spend and revenue and concluded that raising spend will increase revenue. Analyze the dataset and identify the logical flaw in that conclusion.

Given Data

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

A manager states: "The correlation is nearly perfect, so higher ad spend clearly causes higher revenue."

Requirements

Compute the sample correlation between ad spend and revenue.
Test whether the correlation is statistically different from 0 at $\alpha = 0.05$ .
Explain whether statistical significance of correlation is enough to support a causal claim.
Identify the logical flaw in the manager's conclusion.
State what additional analysis or experimental design would be needed to support causality.

Assumptions

Treat the 10 daily observations as paired measurements.
Use a two-sided hypothesis test for correlation.
Assume approximate linearity for the purpose of computing Pearson correlation.
You may assume no arithmetic error in the reported data, but you should question the inference drawn from it.

Sample Data

Example 1

Input

{"alpha":0.05,"sample_size":10,"revenue_thousands":[100,108,116,124,132,140,148,156,164,172],"ad_spend_thousands":[10,12,14,16,18,20,22,24,26,28]}

Output(none)

Problem

Business Context

BrightAds, a mobile commerce company, reviewed recent campaign data and concluded that increasing daily ad spend causes higher same-day revenue because the two variables move together strongly.

Problem Statement

Given Data

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

A manager states: "The correlation is nearly perfect, so higher ad spend clearly causes higher revenue."

Requirements

Compute the sample correlation between ad spend and revenue.
Test whether the correlation is statistically different from 0 at $\alpha = 0.05$ .
Explain whether statistical significance of correlation is enough to support a causal claim.
Identify the logical flaw in the manager's conclusion.
State what additional analysis or experimental design would be needed to support causality.

Assumptions

Treat the 10 daily observations as paired measurements.
Use a two-sided hypothesis test for correlation.
Assume approximate linearity for the purpose of computing Pearson correlation.
You may assume no arithmetic error in the reported data, but you should question the inference drawn from it.

Sample Data

Example 1

Input

{"alpha":0.05,"sample_size":10,"revenue_thousands":[100,108,116,124,132,140,148,156,164,172],"ad_spend_thousands":[10,12,14,16,18,20,22,24,26,28]}

Output(none)

Your answer

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Up next

Correlation vs Causation in Marketing SpendEasy

Evaluate Financial Data with Regression AnalysisMedium

Assessing Advertising Impact on Sales with RegressionMedium

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Correlation Claim in Marketing Spend

Easy

Statistics & Probability

Asked at 7 companies7Hypothesis TestingStatistical SignificanceCausal Inference

Also asked at

Problem

Business Context

BrightAds, a mobile commerce company, reviewed recent campaign data and concluded that increasing daily ad spend causes higher same-day revenue because the two variables move together strongly.

Problem Statement

Given Data

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

A manager states: "The correlation is nearly perfect, so higher ad spend clearly causes higher revenue."

Requirements

Compute the sample correlation between ad spend and revenue.
Test whether the correlation is statistically different from 0 at $\alpha = 0.05$ .
Explain whether statistical significance of correlation is enough to support a causal claim.
Identify the logical flaw in the manager's conclusion.
State what additional analysis or experimental design would be needed to support causality.

Assumptions

Treat the 10 daily observations as paired measurements.
Use a two-sided hypothesis test for correlation.
Assume approximate linearity for the purpose of computing Pearson correlation.
You may assume no arithmetic error in the reported data, but you should question the inference drawn from it.

Sample Data

Example 1

Input

{"alpha":0.05,"sample_size":10,"revenue_thousands":[100,108,116,124,132,140,148,156,164,172],"ad_spend_thousands":[10,12,14,16,18,20,22,24,26,28]}

Output(none)

Problem

Business Context

BrightAds, a mobile commerce company, reviewed recent campaign data and concluded that increasing daily ad spend causes higher same-day revenue because the two variables move together strongly.

Problem Statement

Given Data

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

A manager states: "The correlation is nearly perfect, so higher ad spend clearly causes higher revenue."

Requirements

Compute the sample correlation between ad spend and revenue.
Test whether the correlation is statistically different from 0 at $\alpha = 0.05$ .
Explain whether statistical significance of correlation is enough to support a causal claim.
Identify the logical flaw in the manager's conclusion.
State what additional analysis or experimental design would be needed to support causality.

Assumptions

Treat the 10 daily observations as paired measurements.
Use a two-sided hypothesis test for correlation.
Assume approximate linearity for the purpose of computing Pearson correlation.
You may assume no arithmetic error in the reported data, but you should question the inference drawn from it.

Sample Data

Example 1

Input

{"alpha":0.05,"sample_size":10,"revenue_thousands":[100,108,116,124,132,140,148,156,164,172],"ad_spend_thousands":[10,12,14,16,18,20,22,24,26,28]}

Output(none)

Your answer

Try one AI text evaluation on us

Get structured feedback, scored against a 4-axis rubric. Premium unlocks unlimited.

0 wordstarget ~200

Up next

Correlation vs Causation in Marketing SpendEasy

Evaluate Financial Data with Regression AnalysisMedium

Assessing Advertising Impact on Sales with RegressionMedium

Next question

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172

Day	Ad Spend ($000)	Revenue ($000)
1	10	100
2	12	108
3	14	116
4	16	124
5	18	132
6	20	140
7	22	148
8	24	156
9	26	164
10	28	172