👋 AAM Auxiliary Assumption 5th Pillar

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A.A.M. Auxiliary Assumption Method the 5th Pillar of GrowthOD 📞 Book a GrowthOD Consultation -

Voice: The Scientific Clarifier
Full Pillar Name: Auxiliary Assumptions Method
Who:  Dr. David Trafimow

Main Texts:

·       Trafimow, David. (2020). The role of auxiliary assumptions in scientific inference: Epistemological implications for psychology. Review of General Psychology, 24(2), 147–157. https://doi.org/10.1177/1089268020912075

Introduction: Assumptions Are the Operating System

Every organization runs on assumptions—about success, about people, about data, about reality. These assumptions are rarely seen. They are embedded in strategy decks, policy handbooks, analytics platforms, leadership philosophies, and even questions asked in coaching sessions.

There are Four Auxiliary Assumptions:

1.     Theoretical assumptions are the abstract principles proposed by a model or theory.

2.     Auxiliary assumptions link those theoretical ideas to measurable realities.

3.     Statistical assumptions govern the analytic procedures we employ (e.g., assumptions of normal distribution, independence of errors).

4.     Inferential assumptions underpin the logic we use to draw conclusions from data.

AAM the Auxiliary Assumptions Method, brings those assumptions into the light.

As The Scientific Clarifier of GROWTH OD, A.A.M. equips coaches, consultants, and leaders to apply the logic of falsifiability and epistemic integrity to their thinking. Based on the work of Dr. David Trafimow (2023), A.A.M. is a breakthrough in organizational development and social science—offering a rigorous alternative to belief-driven interventions and unfalsifiable models.

If you're a practitioner of Organizational Development coaching or consulting, Dr. David Trafimow's AAM is a mind-opening learning experience. His Auxiliary Assumptions Method (AAM) offers the missing link between strategy and epistemology. In a field flooded with surface-level fixes and overconfidence in personality tests or survey results, Trafimow's method asks you to pause and examine the assumptions beneath your models. What are you taking for granted about causality, data interpretation, or context? With tools drawn from his rigorously peer-reviewed articles, Trafimow equips you to articulate, test, and refine the invisible scaffolding of your organizational claims. AAM empowers you to coach leaders not just to act—but to think better, with humility and logical clarity. This is what grounds GROWTH OD in scientific integrity. It’s not about charismatic fixes, but principled inquiry. And that’s why every serious consultant should start their week with Trafimow. Find his tools on Google Scholar, in top journals like British Journal of Mathematical and Statistical Psychology, and through your university library. Because real change starts with better assumptions.

Where SEAM diagnoses dysfunction, and AXIOGENICS guides value decisions, A.A.M. asks:

“What assumptions are driving our conclusions—and are they testable, entangled, or invisible?”

This pillar invites organizations into intellectual humility, methodological courage, and systemic honesty.

• P-values have become a crutch for weak data: Trafimow and his co-editor stated that the commonly used threshold of p < .05 is "too easy to pass and sometimes serves as an excuse for lower quality research". This reliance on an arbitrary cutoff can encourage superficial analysis rather than deeper scientific understanding.

• Invalidity of the process: Trafimow described NHST as "blatantly invalid," arguing that if scientists depend on such a process, it should be abandoned entirely. He would prefer no inferential statistics at all over using ones that are known to be problematic.

• Misinterpretation of p-values: Many researchers mistakenly interpret a low p-value as strong evidence against the null hypothesis or as proof that the null hypothesis is false, which is incorrect. P-values only indicate the probability of obtaining data as extreme as observed, assuming the null hypothesis is true—they do not provide the probability that the null hypothesis is actually false.

• Testing against models known to be wrong: Trafimow points out that statistical models are almost never exactly correct in real-world research. Thus, using p-values to test against a null hypothesis embedded in such a model is uninformative: "since the model is wrong, in the sense of not being exactly correct, whenever you reject it, you haven’t learned anything. And in the case where you fail to reject it, you’ve made a mistake". At best, you learn nothing; at worst, you draw incorrect conclusions.

• Historical context: Trafimow notes that experiments and hypothesis testing existed long before p-values were invented, suggesting that organizational development (OD) science can function without them.

As a result of these concerns, Trafimow, as editor of Basic and Applied Social Psychology, implemented a policy banning p-values, confidence intervals, and hypothesis testing from the journal, instead requiring descriptive statistics and effect sizes. He acknowledges that he does not know what statistical approach should replace NHST, but maintains that abandoning a flawed method is preferable to continuing its use.

In summary, Trafimow's position is that p-values and NHST do not provide reliable, meaningful evidence and can mislead researchers, so they should be abandoned in favor of more transparent and descriptive approaches.

Seven Questions for Dr. David Trafimow (see Full Study Guide and References)

1. Why Does AAM Say the Null Hypothesis Should Be Abandoned in OD?

2. Why Does the P-Value Need to Be Abandoned in OD? What Can Replace It?

3. Why Does Adding More Variables to an OD Model Weaken Reliability and Validity? What Is the AAM Alternative?

4. Citations to Three Recent OD Studies Using P-Values—How Would Trafimow Critique Them?

6. Citations to Three Recent OD Studies with Unexamined Statistical Assumptions—AAM’s Challenges

7. Citations to Three Recent OD Studies Violating Falsifiability—AAM’s Focus on Auxiliary Assumptions

The Scientific Roots of A.A.M.

In scientific reasoning, Trafimow (2023) builds on Karl Popper’s idea of falsifiability—the idea that scientific claims must be disprovable to be valid. Trafimow’s method focuses not just on hypotheses, but on the auxiliary assumptions—the invisible premises that hold the whole system up.

Example: A manager concludes, “Productivity is down because employees aren’t motivated.”
The auxiliary assumptions might be:

·       Productivity is the best measure of motivation.

·       Motivation is an individual, not systemic, trait.

·       Environmental factors have not changed.

If even one of these assumptions is false, the entire diagnosis may collapse. A.A.M. helps you find these assumptions, name them, test them, and rethink them.

A.A.M. in Organizational Life

A.A.M. transforms how we approach:

·       Data: What do our metrics assume is valuable? What are they blind to?

·       Strategy: What future do our plans assume is likely, and why?

·       Culture: What stories do we tell about “how things work around here”—and where do those stories come from?

·       Research: What premises shape our survey questions, focus groups, or outcome measures?

Using A.A.M. creates a culture of curiosity instead of certainty—not to paralyze decisions, but to strengthen them.

Four Key Moves of A.A.M.

1.     Surface the Assumption
What beliefs must be true for your conclusion to be valid?

2.     Make the Assumption Testable
Is there a way to check, disconfirm, or challenge this belief?

3.     Check for Entanglement
Is this assumption tied to other unspoken beliefs (e.g., about identity, culture, systems)?

4.     Invite Transformational Inquiry
What would become possible if this assumption changed?

Coaching and Consulting with A.A.M.

Use A.A.M. when:

·       A client keeps hitting the same wall with different strategies.

·       A team is convinced “nothing will ever change.”

·       Leadership decisions seem data-driven—but the data feels biased or incomplete.

·       Culture change efforts plateau due to unspoken norms.

Sample Questions:

·       What do you assume is causing this challenge?

·       What has to be true for that conclusion to hold?

·       How might we disprove that assumption?

·       What’s the story behind this belief—and who benefits from it?

·       If we reversed this assumption, what might shift?

Organizational Case Example: A.A.M. in Action

A major media company experienced sharp Gen Z turnover. Leaders assumed younger workers lacked loyalty. Using A.A.M., the OD team surfaced assumptions:

·       That job stability was still a primary value.

·       That loyalty means tenure, not alignment with purpose.

·       That onboarding was sufficient to create engagement.

They discovered that new hires were leaving due to poor sensemaking rituals and lack of peer coherence. The assumption wasn’t “wrong” morally—but it was incomplete and unfalsifiable as originally stated.

By shifting assumptions, the organization redesigned onboarding to focus on team belonging, story work, and value alignment. Turnover decreased by 18%.

A.A.M. in the GROWTH OD Ecosystem

·       With P.E.R.V.I.E.W.: Stories are built on assumptions. A.A.M. helps test whether those stories are still serving.

·       With SEAM: Hidden costs often arise from hidden assumptions. “Turnover is normal” is an assumption that needs falsification.

·       With AXIOGENICS: The Central Question helps create value; A.A.M. helps ensure the foundation of that value is logically sound.

·       With G.L.O.W.: Even gratitude can be performative if assumptions about emotional labor go untested. A.A.M. invites depth.

Entangled Assumptions: A Special Challenge

Trafimow emphasizes that many assumptions are entangled—knotted together with identity, power, or organizational memory.

Example:

A CEO believes “Leaders must always project confidence.”
The entangled assumptions might include:

·       Vulnerability = weakness

·       Uncertainty = incompetence

·       Emotional expression undermines authority

Using A.A.M., a coach might ask:

·       Where did this assumption originate?

·       What events, people, or cultural messages entangle it?

·       Can we isolate and test just one strand of this belief?

This unraveling process doesn’t just clarify logic—it liberates identity.

Integrating A.A.M. into Practice

1. Falsifiability Audits
Pick one organizational policy or strategic assumption. Ask: Can this be tested? What assumption would falsify it?

2. Assumption Mapping Workshops
In team retreats, chart the assumptions behind major goals. Explore which are testable, which are sacred, and which are historical.

3. Story-Fact Check
In P.E.R.V.I.E.W. coaching, pause to ask: “What assumption is this story built on? And is it still true?”

4. GLOW Alignment Test
Use A.A.M. to explore if the gratitude practices in place are built on trust, or assumption-based performance scripts.

Coaching Prompts Using A.A.M.

·       What needs to be true for your theory to hold?

·       What would change if that weren’t true?

·       What are you assuming about people, systems, or time?

·       How could we design this idea to be falsifiable?

·       What assumption are you most afraid to test?

Final Reflection: A.A.M. as Humility-in-Action

In a time of polarization, noise, and data overload, A.A.M. doesn’t ask us to know more. It asks us to question more skillfully.

Where to Find Trafimow’s Methodological Tools for Auxiliary Assumptions

1. Key Publications by David Trafimow

Trafimow’s frameworks are articulated primarily in his peer-reviewed articles and book chapters. Here are the most relevant and accessible sources:

a. Generalizing across auxiliary, statistical, and inferential assumptions

• Citation: Trafimow, D. (2021). Generalizing across auxiliary, statistical, and inferential assumptions. British Journal of Mathematical and Statistical Psychology, 74(2), 293–308.

• Access: Wiley Online Library - Article Link

• Content: Provides a clear breakdown of how to identify, articulate, and generalize auxiliary assumptions in research.

b. Non Causal Theories and Using Auxiliary Assumptions to Handle Situation‐Specificity

• Citation: Trafimow, D. (2022). Non Causal Theories and Using Auxiliary Assumptions to Handle Situation‐Specificity. Journal for the Theory of Social Behaviour, 52(1), 3–18.

• Access: PhilPapers - Article Link

• Content: Offers practical reasoning frameworks for applying auxiliary assumptions in social science research.

c. Meaning in life research: the importance of considering auxiliary assumptions

• Citation: St Quinton, T., & Trafimow, D. (2022). Meaning in life research: the importance of considering auxiliary assumptions. The Journal of Positive Psychology, 17(1), 1–10.

• Access: Taylor & Francis - Article Link

• Content: Applies the auxiliary assumptions framework to a specific research domain, with step-by-step guidance.

2. How to Use These Tools

• Step 1: Read the above articles to understand the distinction between theoretical and auxiliary assumptions.

• Step 2: Use Trafimow’s logic:

  • Identify all assumptions (theoretical and auxiliary) underlying your hypothesis and methodology.

  • Articulate each auxiliary assumption explicitly.

  • Evaluate: For each, ask: “If this auxiliary assumption were false, would my test of the theory still be valid?”

  • Test: Where possible, empirically or logically test the plausibility of each auxiliary assumption.

• Step 3: Use Trafimow’s syllogistic reasoning (see the 2021 article) to map how auxiliary assumptions connect your data to your theoretical claims.

3. Google Scholar and University Libraries

• Google Scholar: David Trafimow’s Profile
  Search for “auxiliary assumptions” within his publications for the most relevant works.

• University Libraries: Use your institutional access to download full texts if paywalled.

Summary Table

This pillar grounds GROWTH OD in epistemic integrity. It invites a new kind of leadership—not based on control or charisma, but on curiosity, testability, and respect for complexity.

Key Methodological and Theoretical Sources

OD Studies Using P-values

OD Studies with Theoretical Assumptions

OD Studies with Unexamined Statistical Assumptions

Studies Violating Falsifiability (Auxiliary Assumptions Not Explicit)

Studies on P-Values and Statistical Significance (Methodological Critique)

🔍 Your Invitation:
What assumption are you operating from today that feels like a truth?
What if you explored it—not to disprove it, but to deepen your clarity?

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