Truncation biases refer to the errors that occur when data is cut off or truncated in a study or analysis. The spelling of this word is /trʌŋˈkeɪʃən ˈbaɪəsɪz/ (truhng-kay-shuhn buy-uhs-iz), with the stress on the second syllable of "truncation" and the first syllable of "biases." The "tr" sound at the beginning is pronounced as a voiceless alveolar stop, and the "u" in "truncation" is pronounced as "uh" like in "fun," while the "a" in "biases" is pronounced as "ay" like in "day."
Truncation biases refer to systematic errors or distortions that occur in research or data analysis due to the exclusion or limitation of certain observations or data points. This bias arises when researchers or analysts selectively remove or truncate certain parts of the data, leading to an incomplete or skewed representation of the phenomenon being studied.
Truncation biases can occur in various forms, including sample selection biases, attrition biases, censoring biases, or time truncation biases. Sample selection biases occur when certain individuals or groups are deliberately excluded from the sample, resulting in an unrepresentative sample that may not accurately reflect the target population. Attrition biases occur when participants drop out of a study, leading to a loss of data and potentially introducing bias in the analysis. Censoring biases arise when data points are incomplete or limited due to specific conditions or criteria, resulting in incomplete or skewed information. Time truncation biases occur when data is collected only within a specific time period, neglecting earlier or later data points that may be relevant.
Truncation biases can introduce systematic errors and distortions in research findings, leading to inaccurate or incomplete conclusions. To mitigate truncation biases, researchers and analysts should ensure robust sampling methods, minimize attrition or dropouts, consider non-censored or complete data, and include a sufficiently broad time range for more accurate representations of the phenomenon being investigated.
The word "truncation" comes from the Latin word "truncare" which means "to cut off" or "to lop off". It is derived from the Latin word "truncus" meaning "mutilated" or "cut". The word "bias" comes from the Old French word "biais" which means "slanting" or "at an angle". It is derived from the Middle French word "biais", meaning "sideways", and ultimately comes from the Greek word "biazein" meaning "to incline or lean". Therefore, "truncation biases" refers to distortions or errors that occur due to cutting off or eliminating certain data or information, resulting in a slanted or skewed representation of reality.