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Conversion Uplift

Conjunction Fallacy

Definition:

Conjunction Fallacy is a cognitive bias that occurs when people believe that the occurrence of two or more events is more likely than the occurrence of one. The probability of the combined events is actually lower than the individuals.

Examples:

  1. Linda is 31 years old, single, outspoken, and very bright. As a student, she was deeply concerned with issues of discrimination and social justice. Which is more probable? (a) Linda is a bank teller. (b) Linda is a bank teller and is active in the feminist movement.

Many people tend to choose option (b), even though it is impossible for Linda to be both a bank teller and an active feminist. This is an example of the Conjunction Fallacy, as the two events seems more likely, even though it is less probable than the individual event (bank teller) alone.

Conjunction Fallacy Example
  1. An experiment asked participants to rate the likelihood of various events, such as “John goes to the gym at least once a week” and “John goes to the gym at least once a week and reads a fitness magazine regularly.” Participants rated the two events as more likely, even though it includes the individual event.

Evidence:

Research in psychology and behavioural economics has provided evidence of the Conjunction Fallacy in various experimental settings. Studies have shown that people overestimate the likelihood of conjunctions of events compared to the individual ones alone. This is due to the way the human brain processes information and the reliance on mental shortcuts or heuristics, such as the representativeness heuristic, which leads to errors in probability judgment.

Summary:

The Conjunction Fallacy is a cognitive bias that leads people to believe that the occurrence of multiple events is more likely than the occurrence of one, even though the combined probability is lower. This bias occurs due to the reliance on mental shortcuts and heuristics in decision-making, leading to errors in probability judgment.

Useful Resources:

1. Conversion marketing – Glossary of Conversion Marketing.

2. Over 300 tools reviewed – Digital Marketing Toolbox.

3. “Thinking, Fast and Slow” by Daniel Kahneman – Provides an overview of various cognitive biases, including the Conjunction Fallacy, and explains how our thought processes can be influenced by heuristics and biases.

Daniel Kahneman explains the law of small numbers

4. “The Art of Thinking Clearly” by Rolf Dobelli – Explores common cognitive biases, including the Conjunction Fallacy, and provides practical insights on how to improve decision-making by recognizing and mitigating these biases.

the art of thinking clearly

5. A research article by Tversky, A., & Kahneman, D. (1983) originally introduced the Conjunction Fallacy and provides an in-depth analysis and evidence supporting the bias.

6. “The Conjunction Fallacy: A Brief Review of the Literature and Some New Results” – A research article by Tentori, K., & Crupi, V. (2012) that reviews the existing literature on the Conjunction Fallacy and presents new findings on how this bias can be influenced by contextual factors.

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Neal Cole

Neal Cole

Neal is the founder of Conversion Uplift and the author of the official GA4/GTM audit course for CXL. It's the course much of the industry trains on. He's spent over 20 years in digital analytics, including senior roles in online gaming and financial services, working out where businesses' numbers go wrong and what it costs them. His work now focuses on server-side tracking, GA4 and BigQuery: getting the data right, and being the person who's accountable for it when a decision depends on it.
Neal Cole

Neal Cole

Neal is the founder of Conversion Uplift and the author of the official GA4/GTM audit course for CXL. It's the course much of the industry trains on. He's spent over 20 years in digital analytics, including senior roles in online gaming and financial services, working out where businesses' numbers go wrong and what it costs them. His work now focuses on server-side tracking, GA4 and BigQuery: getting the data right, and being the person who's accountable for it when a decision depends on it.

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