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Apophenia

As explained in Demand Forecasting Best Practices

Apophenia is the tendency to perceive meaningful patterns or connections among unrelated events or observations.

Apophenia is the tendency to perceive meaningful patterns among unrelated events. In demand forecasting, it can make random movements in noisy historical sales data look like reliable signals and encourage explanations for every variance. section 16.2.1

Reading noise as a pattern

A forecaster inspecting a historical-demand graph may see a meaningful trend in random fluctuations and become overconfident in judgmental forecasting. After a pandemic, war, or political event, hindsight bias can make the outcome seem predictable all along. section 16.2.1

Reduce apophenia in forecasting by changing how evidence and judgments are reviewed:

  • Focus less on explaining every past forecast error and more on identifying information that could prevent future blind spots. section 16.2.1
  • Use diverse and independently formed judgments, align incentives, and discuss explicit assumptions to limit confirmation bias, anchoring, and groupthink. section 16.4

How Demand Forecasting Best Practices covers it

How deep it goes
Defines the bias, demonstrates two forecasting manifestations, and recommends bias-reduction practices without detailing statistical tests.
Examples
Uses noisy historical-demand graphs, forecast deviations, and major events such as pandemics, wars, and political changes.
What you can do after
Diagnose forecasting judgments by separating random variation from evidence and redirecting reviews toward missing information and assumptions.
How it connects
Connects apophenia with judgmental forecasting, cognitive bias, confirmation bias, anchoring bias, hindsight bias, and groupthink.
Cover of Demand Forecasting Best Practices
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