Cross-Type Amalgamation
Banasiewicz proposes combining anecdotal, research, and statistical evidence by ranking sources for objectivity and relevance to the decision. His doubling rule gives each higher-ranked evidence type twice the weight of the preceding type, then normalizes the weights.
THE IDEA
Cross-Type Amalgamation
Evidence from different sources can point toward different decisions. In Banasiewicz’s framework, anecdotal evidence includes expert opinions and industry benchmarks, research evidence comes from empirical studies, and statistical evidence comes from analyses of organizational data. Each type varies in quality and relevance. A study may be rigorous yet poorly matched to the decision; an internal dataset may be directly relevant yet incomplete.
The aim is to assess these inputs together and reach a decision. When they conflict, Banasiewicz proposes a hierarchy based on objectivity and decision specificity. More objective, more relevant evidence receives greater weight.
His doubling rule ranks the evidence types, assigns each successive type twice the weight of the preceding one, and normalizes the weights to total about 100%. This is a proposed heuristic for choosing weights in the absence of established rules. It still requires judgment about the quality and relevance of each source.
When to use it
Evaluating a new product launch strategy by combining expert opinions, academic research, and internal sales forecasts.
Choosing between two medical treatments using clinical trial data, doctor recommendations, and patient outcome statistics.
Deciding on a business investment by weighting industry benchmarks, peer-reviewed studies, and company-specific financial data.
FROM THEORY TO EVERYDAY LIFE
In practice
Marketing Campaign Decision
A company is choosing between marketing campaigns. Expert opinion favors campaign A, academic research raises concerns about similar campaigns, and internal sales data favors campaign B. Suppose the company ranks these sources in that order for objectivity and relevance.
Weights of 20%, 40%, and 80% would exceed the total allocation. Under the doubling rule, the weights are x, 2x, and 4x, totaling 7x≈100%. Rounding x to about 14% gives rough weights of 14%, 28%, and 56%; these leave a rounding remainder and would need normalization before use. Internal data receives the greatest weight, but the decision still depends on what each source says about the competing campaigns.
Investment Portfolio Selection
An investor is comparing two stocks. Financial blogs favor stock X, research studies give mixed findings, and an analysis of market data favors stock Y. Suppose the investor judges the market analysis most relevant and the blogs least relevant.
Weights of 20%, 40%, and 80% would exceed a total of 100%. If the lowest weight is w, then w+2w+4w=7w=100%, so w≈14.3%. Rounded weights are therefore 14.3%, 28.6%, and 57.1%. The market analysis gets the greatest influence under this rule; its quality and relevance still need assessment.
A MOMENT TO REFLECT
Put it to work.
When faced with conflicting evidence from different sources, how would you rank them by objectivity and decision-specificity?
Apply the doubling rule to a personal decision (e.g., choosing a college or job offer). What weights emerge and do they align with your intuition?
What are the potential pitfalls of using a fixed doubling ratio? When might the hierarchy need to be adjusted?
CONTINUE THE CONVERSATION
Source texts
- Evidence-Based Decision-Making How to Leverage Available Data and Avoid Cognitive Biases (Andrew D. Banasiewicz)