| Course | MGMT 8525M Big Data Decision Making and Management |
|---|---|
| Week | Week 1 |
| Paper type | Discussion post |
| Length | About 540 words, 3 pages |
| Format | Discussion post, APA 7 citations |
| School | Walden University |
| Program | PhD in Management |
| Updated | October 2026 |
Free sample paper for MGMT 8525M Week 1
Week 1 Discussion: What Did the Volume Buy?
Forty Million Orders and Still No Comparison Group: A Discussion Post Sorting What a Subscription Retailer's Huge App-Spending File Bought and What It Could Not, and How Far Its Claim Should Travel
The Claim, Quoted
A composite meal-kit subscription company, Hearthbox, told investors in its latest annual letter: "Across more than 40 million orders, customers who use our app spend 23 percent more per month than those who order on the web. Moving every customer to the app is our top growth lever for next year." The number is large, the file is enormous and the recommendation follows smoothly. This post asks what the 40 million orders bought.
What Volume Bought
They bought precision. With tens of millions of orders, the 23 percent gap is estimated very tightly; the company's quarterly letters show it moving only between 22 and 24 percent. Random noise is not the issue. Lin et al. (2013) made the related statistical point that in very large samples p-values collapse toward zero, so almost any difference becomes statistically significant. A tiny p-value here tells us the gap is not a fluke of sampling. It tells us nothing about why the gap exists.
What Volume Could Not Buy
Volume did not buy a comparison group. Customers chose whether to install the app. People who already order often, cook several nights a week and manage their subscription actively are exactly the people likely to download an app for it. If heavy spenders selected the app, the 23 percent gap measures who chose the app at least as much as what the app does. Moving light spenders onto the app may not make them heavy spenders. Forty million orders cannot fix this, because every one of them carries the same self-selection.
The Rival View
Anderson (2008), in a widely read Wired essay, argued that with enough data, correlation is enough and the scientific method of hypothesis, model and test becomes obsolete. The Hearthbox letter reasons in that spirit. Kitchin (2014) answered that big data does not speak for itself: data are produced by particular systems for particular purposes, and their patterns still need theory and design to be interpreted. I side with Kitchin here, because the self-selection problem is a design problem, and no amount of the same data solves it.
How Far the Claim Should Travel
The claim can travel this far: app users currently spend more than web users. It cannot yet travel to the recommendation. A randomized trial, such as offering an app incentive to a random half of web customers, would show whether the app changes spending. Until then, the honest version of the letter's sentence is descriptive: app users spend more. The growth plan would be a bet on a cause that the data have not yet shown, and the trial would cost far less than a failed company-wide migration.
Reply to Grace
You argue that a hospital system's 2 million patient records prove its new triage tool reduces wait times. What did the volume buy you, and was there a comparison period before the tool?
Reply to Sam
Your retailer's loyalty data covers every member transaction for five years. Does it cover anyone who is not a member, and does the recommendation affect them?
References
Anderson, C. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/
Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1). https://doi.org/10.1177/2053951714528481
Lin, M., Lucas, H. C., Jr., & Shmueli, G. (2013). Too big to fail: Large samples and the p-value problem. Information Systems Research, 24(4), 906-917. https://doi.org/10.1287/isre.2013.0480
Reading the MGMT 8525M Week 1 instructions
Your opening post examines one claim that rests on a very large dataset and separate what the size of the data improved from what it left untouched. Quote the claim exactly, from an annual report, a press release or a business article. Explain what the volume bought, usually precision, and what it could not buy, usually a valid comparison or a measure of the right thing. Bring in the statistical point that very large samples make almost any difference statistically significant, and engage the argument that enough data makes models unnecessary. End by stating how far the claim deserves to travel. Plan on roughly four hundred to five hundred words for the main post, then two replies, each asking a classmate what their file's volume actually purchased.
How the MGMT 8525M Week 1 example is put together
The sample quotes the meal-kit company's investor letter, with its 40 million orders and 23 percent gap. The second section shows that the estimate is stable from quarter to quarter and uses the research on p-values in large samples to explain why significance tells us only that the gap is not a sampling fluke. The third section names self-selection: customers who already spend heavily are the ones likely to install the app. A rival section presents the end-of-theory argument and Kitchin's reply that data are produced by systems and still need theory and design. The post limits the claim to a description of current app users and proposes a randomized incentive test. Each of the two replies turns that question on a classmate's own file.
MGMT 8525M Week 1 rubric: what earns full marks
Most of the credit in this post goes to the separation of precision from validity. Posts that praise or dismiss big data in general score in the middle, because the assignment asks about one claim. Graders look for the claim quoted exactly, a clear account of what the volume improved, a specific reason it could not fix the comparison and accurate use of the large-sample significance point. The rival view should be represented fairly before it is answered. A statement of how far the claim can travel, with a test that would extend it, shows judgment. Credit for replies depends on a sharp question about the classmate's data. Correct citations of the essay and the articles complete the Week 1 grade.
Common MGMT 8525M Week 1 mistakes, and how to avoid them
The weak version of this post argues that big data is overhyped without engaging any specific claim. Find a sentence that cites a large number of records and draws a conclusion, and quote it. Ask two questions: would the estimate change much with half the data, and would it change if the groups being compared had been formed at random? The first is about precision; the second is about validity. Explain the difference in your own words. Read the end-of-theory essay itself and give its strongest point. Close with what the claim can safely say today. If you cannot find a good claim, investor letters and product announcements are good sources, and a tutor can help you choose one. The meal-kit post above shows the full structure.
Get a MGMT 8525M Week 1 example written to your instructions
Send the Week 1 prompt and the rubric your classroom posted, along with how many replies are due, and a finished post with replies returns in 24-48h, the first one free. Got a big-number claim in mind from work or the news? Mention it, and the argument is built on that example instead of the retailer used here.
More MGMT 8525M papers and PhD in Management samples
- MGMT 8525M Week 2: Data Profile Brief
- MGMT 8525M Week 3: Measurement Critique
- MGMT 8525M Week 4: Inference Memo
- MGMT 8525M Week 5: Sampling Analysis
- MGMT 8525M Week 6: Dashboard Critique
- MGMT 8525M Week 7: Prediction Case
- MGMT 8525M Week 8: Stewardship Memo
- MGMT 8525M Week 9: Peer Replies
- MGMT 8525M Week 10: Decision Brief
- MGMT 8525M Week 11: Research Paper
- MGMT 8425M Week 1: Orientation Post
- MGMT 8415M Week 1: Opening Post
MGMT 8525M Week 1 questions, answered
Where can I find a free MGMT 8525M Week 1 sample paper?
The meal-kit app spending post, with two replies, is posted above as the Week 1 sample.
What does a large dataset buy in the MGMT 8525M Week 1 sample?
Precision, since the 23 percent gap barely changes between quarters, but not a valid comparison group.
Why doesn't a tiny p-value settle the claim?
In very large samples almost any difference becomes significant, so the p-value says nothing about why the gap exists.
What is the end-of-theory argument?
Chris Anderson's 2008 claim that enough data makes correlation sufficient and the scientific method unnecessary.
How far can the app spending claim travel?
It describes current app users; a randomized incentive test would be needed before moving everyone to the app.