GB513 Unit 6 Hypothesis Test Memo Example

Reviewed by Chester Goodwin, MBA Business Analytics Purdue University Global Free custom sample in 24 to 48h

This GB513 Unit 6 hypothesis test memo checks a vendor's claim that new scheduling software cut a composite regional bank's average teller-line wait below five minutes. Purdue Global GB 513, Business Analytics, asks MBA students to report a test in memo form for a manager. Forty timed visits at two pilot branches average 4.6 minutes with a standard deviation of 1.7. A one-tailed t test gives a statistic of about negative 1.49 and a p-value of about 0.072, so the bank cannot conclude the claim holds at the 0.05 level. The memo explains how visits were timed, why the threshold should not move, what a type II error would mean and why about 112 visits are needed.

CourseGB513 Business Analytics
UnitUnit 6
Paper typeHypothesis test memo
LengthAbout 740 words, 5 pages double-spaced
FormatAPA 7 student paper
SchoolPurdue University Global
ProgramMBA
UpdatedOctober 2026

Free sample paper for GB513 Unit 6

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Not Yet Below Five Minutes: A Unit 6 Hypothesis Test Memo on a Vendor's Wait-Time Claim

[Student Name]

Purdue University Global

GB513: Business Analytics

Unit 6 Hypothesis Test Memo

[Instructor Name]

[Date]

The bank, the vendor, the pilot and every figure are composite and illustrative; the methods cited are real.

What this part is doingStating the result and its likely cause in the lead tells the vice president what to do before the statistics.
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Memo

To: Janelle Ruiz, Vice President, Retail Operations, Prairie Mutual Bank From: Analytics Associate, Retail Operations Date: October 15 Subject: Test of the vendor's claim that average teller wait is now below five minutes

How the Visits Were Timed

The 40 visits were selected at random times across two weeks at the two pilot branches, 20 at each, covering mornings, lunch hours and afternoons in proportion to each period's normal traffic. A branch employee recorded the time each selected customer joined the line and the time they reached a teller. Visits during the first three days after installation were excluded, because staff were still learning the new schedules and waits then would not reflect normal operation.

What this part is doingDescribing how the sample was drawn lets the vice president judge whether the 40 visits represent normal service.
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Conclusion

The pilot data do not provide sufficient evidence, at the 0.05 significance level, that average teller-line wait at the two pilot branches has fallen below five minutes. The sample average was 4.6 minutes, which is promising, but 40 visits are too few to rule out that the true average is still five minutes or more. I recommend extending the pilot one month to collect about 112 timed visits before deciding on a full rollout.

The Test

Hypotheses. H0 holds that the true average wait is still five minutes or longer; H1 holds that it has dropped below five. Written with symbols, H0 is mu at or above 5 and H1 is mu under 5. This is a one-tailed test, because the vendor's claim is directional.

Significance level: 0.05.

Sample: 40 randomly selected customer visits across both pilot branches, timed from joining the line to reaching a teller.

Sample mean: 4.6 minutes. Sample standard deviation: 1.7 minutes.

Test statistic: t equals (4.6 minus 5) divided by (1.7 divided by the square root of 40), or about negative 1.49, with 39 degrees of freedom.

Critical value: negative 1.685. P-value: about 0.072.

Because the p-value of 0.072 is greater than 0.05, and the test statistic does not fall below the critical value, we fail to reject the null hypothesis (Anderson et al., 2020).

The pilot average is below five minutes, but with only 40 visits, a gap that small could have appeared by chance even if nothing had changed.

What this part is doingThe highlighted sentence explains the result in plain terms for a reader who will not read the statistics.
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What the Result Means for the Bank

Failing to reject the null does not prove the software has no effect. It means the pilot has not yet produced enough evidence to back the vendor's claim. Ronald Wasserstein and Nicole Lazar warn that a p-value above a threshold should not be read as proof that there is no effect, and that results should be reported with their context and size (Wasserstein & Lazar, 2016). The observed improvement, about 0.4 minutes, may be real.

The risk here is a type II error: concluding the software does not work when it actually does. With 40 visits and this much variation, the test has limited power to detect an improvement as small as 0.4 minutes. Rolling out the software everywhere on this evidence would be premature, but abandoning it would risk missing a real gain.

Why Not Simply Relax the Significance Level

It would be possible to reach significance by testing at 0.10 instead of 0.05, since the p-value is 0.072. Choosing the level after seeing the data would undermine the test, because any result can be made significant by moving the threshold. The 0.05 level was set before the pilot began and should stand. The better response is more data, which will either confirm the improvement or show it was chance.

Recommendation and Sample Size Target

Extend the pilot by one month at the same two branches and time about 112 visits. That sample size gives roughly an 80 percent chance of detecting a true reduction of 0.4 minutes at the 0.05 level, assuming the standard deviation stays near 1.7 minutes (Levine et al., 2021). If the extended pilot confirms the result, the bank can negotiate the full rollout with evidence; if not, it can hold the vendor to its claim.

The Excel output for the test, from a one-sample calculation sheet, is attached, together with the raw timing log for all 40 visits so the figures can be checked.

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References

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2020). Statistics for business and economics (14th ed.). Cengage.

Levine, D. M., Stephan, D. F., & Szabat, K. A. (2021). Statistics for managers using Microsoft Excel (9th ed.). Pearson.

Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129-133. https://doi.org/10.1080/00031305.2016.1154108

The Purdue Global GB513 Unit 6 instructions

For GB513 Unit 6 you test a claim and write up the result as a memo for the person who must decide. Open with the conclusion in plain language, followed by a recommendation. Describe how the sample was collected. Present the test compactly: hypotheses in words and symbols, significance level, sample statistics, test statistic, critical value and p-value. Explain the decision in the reader's terms. If you fail to reject the null, explain that this is not proof of no effect, discuss the risk of a type II error and resist changing the significance level after seeing the data. Recommend a next step, such as a larger sample, and calculate the size needed. Attach the Excel output. Keep it to about two pages in memo form, citing statistics sources in APA 7.

How this GB513 Unit 6 example is built

The memo to Prairie Mutual Bank's vice president of retail operations opens with the conclusion and a recommendation to extend the pilot. A section explains that 40 visits were timed at random across two weeks, excluding the first three days after installation. The test sets H0 at a mean of five minutes or more against H1 below five, at the 0.05 level, with a t statistic near negative 1.49, a critical value of negative 1.685 and a p-value near 0.072, citing Anderson and colleagues; the highlighted sentence explains the result. Wasserstein and Lazar caution against reading the result as no effect. A section explains why the bank should not switch to 0.10 after seeing the data. A target of about 112 visits for 80 percent power follows, citing Levine, Stephan and Szabat, with the output and timing log attached.

GB513 Unit 6 rubric: what earns full points

GB513 instructors grade this memo on a correctly set up test and a conclusion a manager can act on. The conclusion should come first, in plain language. Hypotheses should match the claim's direction, with the null and alternative stated correctly. Test statistic, critical value and p-value should be accurate. Explaining that failing to reject is not proof of no effect, and discussing type II error, shows statistical maturity. Holding the significance level fixed shows integrity. A recommendation with a sample size target turns the result into a plan. Memo form suits the reader. The APA 7 list joins the textbook sources for the t test and power with Wasserstein and Lazar's guidance on reading p-values.

Common GB513 Unit 6 mistakes, and how to avoid them

Many GB513 Unit 6 memos report the test in textbook form and leave the manager to work out what it means. Others conclude that the software does not work because the null was not rejected. A common gap is power: a small sample may be unable to detect a real but modest improvement, and the memo should say so. Some writers change the significance level to reach a result. Lead with the conclusion, describe the sample, set the hypotheses in the claim's direction, report the test compactly, explain the result and its limits and recommend a sample size.

Get a GB513 Unit 6 example written to your instructions

Whatever claim your GB513 Unit 6 scenario tests, pass the data, the prompt and the rubric to the desk. Within 24 to 48 hours a custom memo is ready, its hypotheses worded before the numbers and its recommendation matched to the strength of the result. You pay nothing for the first. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.

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GB513 Unit 6 questions, answered

Which hypothesis should contain the claim being tested?

The claim that requires evidence belongs in the alternative. When a vendor, a manager or a campaign asserts an improvement, the null keeps the status quo and the data must overturn it. Some prompts phrase the claim as an equality, and then it sits in the null. A finished memo explains its choice in one line, which prevents most setup deductions.

Is a p-value of 0.09 close enough to call the result significant?

Not at a 0.05 level set in advance. Changing the level after seeing the result undermines the test, and instructors spot it. What a memo can do is report the p-value honestly, say the evidence is suggestive but insufficient, and recommend gathering more data. That reads as sound judgment rather than a failed test.

Does the memo need an APA title page?

Usually not. A memo format replaces the title page with its header block, and many sections accept references at the end in APA style if sources are cited. Check the rubric, since a few instructors want the memo embedded in a formal paper. The example follows the memo convention because its prompt frames a manager as the reader.

Where can I find a free GB513 Unit 6 sample paper?

This page carries a full GB513 Unit 6 hypothesis test memo on a vendor's wait-time claim, with a one-tailed t test, an explanation of type II error, a sample size target and APA 7 references.

Why not test at the 0.10 level instead?

The p-value of about 0.072 would pass at 0.10, but choosing the level after seeing the data would make any result reachable. The 0.05 level was set before the pilot, so the better response is to collect more data.