Bias

Understanding Bias

• Bias refers to any systematic error that produces results which diverge from the true values.
• It’s important to grasp that bias can distort the outcome of an experiment or survey.
• While random errors balance out over large numbers of trials, bias influences all results in one direction.
• Understanding and identifying bias helps in making fair and reliable decisions based on data.

Types of Bias

• Selection bias: This occurs when the sample chosen for a study or experiment does not accurately represent the population.
• Measurement bias: This happens when the method of collecting data encourages a certain outcome.
• Response bias: This takes place when participants in a study or survey give inaccurate responses due to factors like question wording or interviewer behaviour.

Avoiding Bias

• Implement random selection of samples to ensure that every member of a population has an equal chance of being picked.
• Take measures to reduce response and measurement bias, such as using clear, concise, and neutral language in questionnaires, and employing accurate measurement tools.
• Where unavoidable, bias should be recognized and its impact considered when using data to make decisions.

Bias in Probability and Statistics

• In all aspects of collecting and interpreting data, bias skews results and provides a faulty understanding of situations.
• An understanding of bias is crucial to accurate statistical analysis and probability prediction.
• At its simplest, bias can make the less probable event more probable and the more probable event less probable.
• Always considering the potential for bias is key to accurate predictions and decisions based on data.