Types of Biases

Hawthorne Effect

subjects may act differently when they know they are being watched (for better or worse)

ex: increased hand washing compliance among healthcare workers when they know they are being monitored

Rosenthal Effect (“researcher rosenthal)

the researcher wants their study to be successful or “correct,” so they design their test/experiment/etc to increase their likelihood that their data supports their hypothesis

ex: teachers telling certain students they had high potential leading those student to perform between because the teachers elevated their expectations and gave more positive feedback

Barnum Effect (barnum circus with tent that fits everyone)

data presented in a way that is too broad and skews the results

ex: fortune tellers who use vague statements so there is a higher probability for at least one person to match what is being told

Hindsight Bias

the tendency to see past events as more predictable than they actually were (make sure you have the data to support your findings even if the results “seem” right)

ex: after a sports game when fans say “I knew they were going to win” even though the game’s outcome may have been completely unknown with many factors influencing the result.

Confirmation Bias (taking quotes out of context)

interpreting data in a way or selecting only the evidence that supports our existing beliefs

ex: during an election, a voter might dismiss emerging facts about their favored candidate poor behavior while actively seeking out information that portrays the opposing candidate.