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Bias01:22

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
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When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Sample size determination with a pilot study.

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Related Experiment Video

Updated: Nov 14, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Optimal guessing in 'Guess Who'.

Ben O'Neill1

  • 1College of Science and Medicine, Australian National University, Canberra ACT, Australia.

Plos One
|March 10, 2021
PubMed
Summary
This summary is machine-generated.

This study analyzes the children's game Guess Who, applying game theory to find an optimal strategy. The research reveals a first-mover advantage and provides mathematical insights for winning the game.

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Area of Science:

  • Game Theory
  • Mathematical Strategy

Background:

  • The popular children's game Guess Who involves deductive reasoning.
  • Understanding optimal strategies can enhance gameplay.
  • Previous analyses have not fully explored the game's mathematical underpinnings.

Purpose of the Study:

  • To formally derive an optimal strategy for the game Guess Who.
  • To determine win-probabilities and analyze the first-mover advantage.
  • To generalize the game's analysis beyond its standard rules.

Main Methods:

  • The study treats Guess Who as a zero-sum symmetric game with perfect information.
  • Mathematical modeling and formal derivation of optimal strategies were employed.
  • Analysis was extended to generalized game states with numerous characters.

Main Results:

  • An optimal strategy for Guess Who was formally derived.
  • Win-probabilities were calculated, indicating a first-mover advantage.
  • The mathematical framework allows for analysis of larger, generalized game states.

Conclusions:

  • The optimal strategy provides a clear path to winning Guess Who.
  • Mathematical insights confirm the strategic importance of going first.
  • The generalized model offers a robust framework for analyzing similar perfect information games.