| Course | DBA-8000 Critical Thought and Informed Action Models of Decision Making and Organization Achievement |
|---|---|
| Module | Module 2 |
| Paper type | Module 2 written assignment: psychological traps and bias |
| Length | About 581 words, 5 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | Doctor of Business Administration |
| Updated | October 2026 |
Free sample paper for DBA 8000 Module 2
Anchors, Sunk Costs and a Confident Forecast: Auditing the Core Lab Decision for Bias
Student Name
Doctor of Business Administration, Thomas Edison State University
DBA-8000: Critical Thought and Informed Action: Models of Decision Making and Organization Achievement
Mentor Name
Month Day, Year
Anchors, Sunk Costs and a Confident Forecast: Auditing the Core Lab Decision for Bias
Why Revisit a Decision Already Analyzed
Module 1 found that building an automated core laboratory had the highest expected value. But every number in that matrix came from people, and Tversky and Kahneman (1974) showed that people estimating uncertain quantities rely on heuristics, such as anchoring and availability, that are often useful but produce systematic errors. A formal analysis can carry those errors forward with an air of precision.
Trap 1: Anchoring on the Vendor's Throughput
The core lab's payoff in the high-growth state rests on throughput: how many tests an hour the automated line can process. Our estimate began with the vendor's figure of 4,800 tests an hour. Tversky and Kahneman showed that a starting number, even one chosen at random, drags later estimates in its direction. Reference sites I later called reported sustained rates of 3,600 to 4,100, because of maintenance, reruns and sample mix. Starting from 4,800 likely inflated our payoff estimate by about 10 percent.
Trap 2: Sunk Cost in Recent Analyzers
Two of our hospital laboratories installed new chemistry analyzers eighteen months ago at a cost of $2.4 million. In planning meetings, site directors argued against consolidation partly because "we just bought those." In experiments by Arkes and Blumer (1985), people kept putting money into a failing course of action because of what they had already put in, even though future returns did not warrant it. The analyzers' cost is spent whatever we decide; only their resale or redeployment value belongs in the analysis.
Trap 3: Confirmation in the Volume Forecast
The planning team that estimated a 30 percent chance of high growth is the same team that proposed the core lab. Its forecast emphasized two affiliation talks that would raise volume and gave less weight to a payer's plan to steer outpatient work to a national laboratory. Hammond et al. (1998) describe the confirming-evidence trap: seeking information that supports an existing preference and discounting what contradicts it.
Trap 4: Overconfidence in the Probabilities
The probability estimates, 0.3, 0.5 and 0.2, were stated without ranges. Overconfidence shows up as estimates that are too precise. Asked for a range on the low-growth probability, team members gave answers from 0.1 to 0.35. At 0.35, building's expected value falls to about 5.8, close to upgrading's.
Safeguards
Kahneman et al. (2011) proposed a pre-decision check in which leaders test a team's proposal for bias, asking whether its authors stand to gain, whether rival options got a fair hearing and whether the base case is too rosy.
| Trap | Safeguard |
|---|---|
| Anchoring | Build throughput estimates from reference sites first, then compare with vendor claims |
| Sunk cost | Record the current analyzers only at resale or redeployment value |
| Confirmation | Ask a finance analyst outside the planning team to write the strongest case against building |
| Overconfidence | Require ranges for every probability and test the decision across them |
| All four | Hold a premortem: imagine the core lab has failed five years from now and list why |
The Revised Picture
With reference-site throughput, recent analyzers valued at redeployment and a low-growth probability of 0.25, building still has the highest expected value, about 6.5 against upgrading's 5.2, but the margin has narrowed, and the case for a phased build that begins with chemistry has strengthened.
Conclusion
The formal analysis was sound in structure but vulnerable in its inputs. Naming the traps, and building safeguards into the process, makes the recommendation more honest and more robust.
References
Arkes, H. R., & Blumer, C. (1985). The psychology of sunk cost. Organizational Behavior and Human Decision Processes, 35(1), 124-140. https://doi.org/10.1016/0749-5978(85)90049-4
Hammond, J. S., Keeney, R. L., & Raiffa, H. (1998). The hidden traps in decision making. Harvard Business Review, 76(5), 47-58.
Kahneman, D., Lovallo, D., & Sibony, O. (2011). Before you make that big decision. Harvard Business Review, 89(6), 50-60.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131. https://doi.org/10.1126/science.185.4157.1124
DBA 8000 Module 2 instructions, in plain terms
This written assignment covers psychological traps and bias in decision making, and Module 2 also includes a brief reflection on the topics that interest you most. Expect to identify heuristics and biases at work in a real organizational decision, explain each with research, show how it affected the analysis and propose safeguards. Use a decision you know well, ideally one you have analyzed formally, so you can show how bias enters the numbers. Doctoral-level work should engage the primary research, not only summaries of it. Re-run any formal analysis after correcting for the biases you find, so the reader sees how much they mattered, and include safeguards that fit how your organization actually decides. Engage the primary research.
How the DBA 8000 Module 2 example is put together
The paper explains why a formally analyzed decision still needs a bias audit. Four sections examine anchoring, sunk cost, confirmation and overconfidence, each with research and evidence from the decision. A table matches each trap to a safeguard, drawing on research on reviewing decisions for bias. A revised analysis shows the recommendation holds but with a narrower margin, and the conclusion distinguishes structure from inputs. The overconfidence section re-runs the expected value analysis with the team's own range of probabilities, and the revised picture shows the recommendation holding with a narrower margin. A table pairs each trap with a safeguard drawn from research on reviewing decisions. Every trap is shown with evidence from the decision itself.
DBA 8000 Module 2 rubric: what earns full marks
Mentors look for a correct reading of the heuristics and biases research, a persuasive link to one live decision, safeguards a team could actually use and writing at the scholarly level. Papers that show how a bias changed specific numbers demonstrate depth. Engaging the primary studies, rather than secondary summaries, meets doctoral expectations. Re-running the analysis after correcting for bias shows the practical value of the module. Quantifying how much a bias changed the result, rather than asserting it, shows analytical rigor. Safeguards matched to specific traps, including a premortem, show practical judgment. Engaging the primary studies, such as the original experiments on anchoring and sunk cost, meets the standard expected of doctoral work.
DBA 8000 Module 2 help: mistakes that cost marks
Students lose points by listing biases without evidence they occurred, by describing biases only in others or by proposing safeguards too general to use. Others cite popular books instead of research. Show evidence for each bias in your decision. Include your own. Quantify the effect where you can. Match each bias to a specific safeguard. Re-run your numbers after correcting each bias, and match every bias to a safeguard someone in your organization will actually use. Include the biases in your own reasoning, not only in others'. A free first sample on your own DBA-8000 bias assignment is available on request. Tie each bias to a document, a meeting or a number from the case, so the audit reads as evidence.
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DBA 8000 Module 2 questions, answered
Where can I find a free DBA 8000 Module 2 sample paper?
A bias audit of a laboratory network's core lab decision is the assignment above.
What is anchoring?
The tendency for an initial value to pull later estimates toward it, even when it is arbitrary or unreliable.
What is the sunk cost trap?
Continuing a course of action because of what has already been spent rather than what future returns justify.
What is a premortem?
A team pretends the decision has already gone badly and writes down the likely reasons, which surfaces quiet risks early enough to change course.
How does overconfidence appear in forecasts?
As estimates stated too precisely, without ranges that reflect real uncertainty.