| Course | GB701 Business Statistics |
|---|---|
| Unit | Unit 6 |
| Paper type | Seminar reflection |
| Length | About 800 words, 5 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | MBA |
| Updated | October 2026 |
Free sample paper for GB701 Unit 6
Significant and Unimportant at Once: A Unit 6 Seminar Reflection on Friday Quotes
[Student Name]
Purdue University Global
GB701: Business Statistics
Unit 6 Seminar Reflection
[Instructor Name]
[Date]
The distributor, its quotes and every figure are composite and illustrative; the sources cited are real.
What I Believed Going In
These were my notes before the session: "Friday quotes take longer to convert. Welch t = 3.08, p = .002, n = 52,410 quotes. Recommend moving quote reviews to Thursdays." I had found the result while exploring quote-level data for my study, and I was pleased with it. A p value of .002 seemed to settle the matter. I had already drafted a sentence for my sales leadership: "Quotes sent on Fridays convert significantly more slowly."
The Question That Changed the Discussion
Our instructor asked each of us who had brought a significant result to compute one more number: the effect size. Mine was a standardized difference d of 0.035. The two distributions of conversion times, Friday and other days, overlap almost completely. The Friday flag explains about 0.02 percent of the variation in conversion time. The instructor's point was simple: with fifty thousand observations, almost any difference will be statistically significant, and the p value tells you only that the difference is unlikely to be exactly zero. It does not tell you whether the difference matters. Wasserstein and Lazar (2016), writing for the American Statistical Association, make the same point: p tells you how surprising the data are under a hypothesis of exactly zero difference, not how big the difference is.
Putting the Gap in Dollars
A classmate who works in treasury asked what 0.21 days was worth. We worked it out together. Orders from Friday quotes total about $83 million a year. Receiving them 0.21 days later delays that revenue by about $48,000 on an average day, and at an 8 percent cost of capital the delay costs roughly $3,800 a year. Rescheduling every quote review in the sales department to move away from Fridays would cost far more in staff time than it could save.
A difference can be real, measured precisely and still not worth anyone's time.
Clustering Checked, Not Assumed Away
A second classmate raised a different problem. My test treated 52,410 quotes as independent, but quotes come from accounts, and an account that tends to be slow is slow on every day of the week. Treating clustered observations as independent understates the standard error. Cameron and Miller (2015) explain that when observations are grouped, standard errors should be computed at the level of the group. When I reran the comparison after the session with standard errors clustered by account, the p value rose to .041. The difference remained statistically significant at the conventional level, but less convincingly, and its effect size was unchanged.
What I Changed
Looking back, I can see why the result felt important. I had spent days cleaning the quote-level data, and a significant result felt like a reward for the work. The p value was also the only number in my notes that came with a ready-made verdict, significant or not, while the effect size required me to decide what counts as large. The seminar's lesson was that the second judgment is the one that matters for a business decision, and that no software output can make it for me.
I rewrote the sentence for sales leadership: "Friday quotes convert about a fifth of a day more slowly than quotes issued on other days, a difference too small to justify changing the review schedule." The finding did not disappear; it was put in proportion.
A Reporting Rule for the Rest of the Term
I adopted a rule for the remaining units. Every test result I report will appear with an effect size in the original units, here days, with a confidence interval, and with a sentence judging whether the difference would change the decision at hand. Where data are clustered, standard errors will account for it. Ziliak and McCloskey (2008) argue that business and economics have long confused statistical significance with practical importance, and the rule is my attempt not to repeat that confusion in my own study.
What Would Change My Mind
If the Friday delay were concentrated in a few large special-order quotes, where a day's delay on a single quote could be worth tens of thousands of dollars, the finding might matter for those quotes even though it is trivial on average. I plan to check that in the regression units, where order type is already a predictor. If it turns out to hold, the right response would be a narrow one, such as faster review of large special-order quotes issued late in the week, not a change to everyone's schedule.
References
Cameron, A. C., & Miller, D. L. (2015). A practitioner's guide to cluster-robust inference. Journal of Human Resources, 50(2), 317-372. https://doi.org/10.3368/jhr.50.2.317
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
Ziliak, S. T., & McCloskey, D. N. (2008). The cult of statistical significance: How the standard error costs us jobs, justice, and lives. University of Michigan Press.
What the GB701 Unit 6 assignment asks for
GB701 Unit 6 centers on one result, significant yet perhaps too small to matter, discussed in seminar or through the written route. Quote the result and the conclusion you drew before the session. Report the questions or computations the session added, such as the effect size, a practical conversion into business units or a check on the independence of observations. Explain how your view changed and connect the change to a source on statistical reporting. Show the rewritten conclusion. State a rule you will follow for the rest of the course. Finish by naming the evidence that would reopen the question. First person suits the genre; 750 to 900 words with APA 7 citations is the usual GB701 length.
How the GB 701 Unit 6 example is put together
The reflection quotes the candidate's pre-session notes and draft sentence. The instructor's question about effect size yields d of 0.035, and Wasserstein and Lazar's statement on p values explains why significance does not mean importance. A classmate prices the 0.21-day gap at about $3,800 a year, and the highlighted sentence says a real difference can still be worthless. A second classmate raises clustering, and the rerun with account-clustered standard errors, citing Cameron and Miller, gives p of .041. A section admits why the p value felt persuasive. The rewritten sentence follows, a reporting rule cites Ziliak and McCloskey, and a closing paragraph names a narrow case where the delay might matter.
Reading the GB701 Unit 6 grading rubric
GB701 instructors give the most points in this reflection for a visible change in reasoning. Quoting the original result and conclusion earns credit because the change can be measured. Reporting the effect size and a practical conversion shows the writer applied the session's lesson. Recognizing and addressing non-independence demonstrates statistical maturity. Connecting the shift to a source on reporting practice shows the reflection is grounded. A rewritten conclusion and a concrete reporting rule show the lesson will last beyond the session. Naming what would reopen the question completes the reflection, and GB701 graders also reward a reflection whose new reporting rule is specific enough to be checked in later units, and first-person clarity with APA 7 citations finishes the grade.
Common GB701 Unit 6 mistakes, and how to avoid them
Many GB701 seminar reflections state that statistical significance is not practical significance without ever computing an effect size. Others swing to dismissing the result entirely when a narrower conclusion fits. A common gap is ignoring clustering when observations come from repeated units such as accounts or stores. Writers often describe the session without quoting what they believed before. The practical conversion into dollars or days is frequently skipped, though it is what managers understand. Quote your original claim, compute the effect size, convert it to business terms, check independence, rewrite the conclusion and set a reporting rule. Our team can write a free custom seminar reflection on your result.
Get a GB701 Unit 6 example written to your instructions
Pass along what you noted during the Unit 6 seminar, or, if attending live was impossible, the questions posed instead, plus the rubric that applies. Your custom reflection, back within 24-48h, is built on a result of your own and shows how your reading of it changed. There is no fee for the first sample. 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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GB701 Unit 6 questions, answered
Can the Unit 6 reflection be written if the seminar was missed?
In many sections a written alternative is offered for candidates who cannot attend live, and the instructions describe its form. It often asks for a response to the recorded session or to a set of questions. The reflection then draws on the recording's content, and it should say so plainly rather than implying attendance.
Is first person acceptable in a doctoral reflection?
Yes, and it is expected here, because the genre records how the writer's reasoning moved. The first person should carry specific claims, what was believed, which evidence altered it and the practice that follows, rather than feelings about the session. Scholarly tone and first person sit together comfortably when every sentence carries information.
How technical should a seminar reflection be?
Technical enough to show the reasoning, not so technical that it becomes a second analysis. Reporting the test statistic, the effect size and the practical conversion in a sentence each is about right. Tables are uncommon in reflections, and a paragraph that walks through one result in plain terms tends to earn more than several results listed.
Where can I find a free GB701 Unit 6 sample paper?
A complete GB701 Unit 6 seminar reflection sits on this page, turning a significant 0.21-day Friday delay into a reported effect size, a dollar value and a clustered test, with a reporting rule.
Why does clustering change the p value?
Quotes issued to one account share that account's habits, so fifty thousand quotes hold far less independent evidence than fifty thousand separate customers would. Clustered standard errors reflect that and are usually larger.