Are absolute frequencies, relative frequencies, or both effective in reducing cognitive biases?
✍ Scribed by Clare Harries; Nigel Harvey
- Book ID
- 101347629
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 141 KB
- Volume
- 13
- Category
- Article
- ISSN
- 0894-3257
No coin nor oath required. For personal study only.
✦ Synopsis
Biases in probabilistic reasoning are a}ected by alterations in the presentation of judgment tasks[ In our experiments\ students made likelihood judgments that an event was produced by various causes[ These judgments were made in terms of probability\ relative frequency or absolute frequency on a full or a pruned list of causes[ When they had little personal experience of the event "causes of death#\ the pruning bias was smaller with relative frequencies than with absolute frequencies or probabilities[ When they had more personal experience of the event "missing a lecture#\ the bias was less with both types of frequency than with probability but still lowest with relative frequency[ We suggest that likelihood information is usually stored as relative frequencies when it has been obtained from public sources but that it is based on event counts when it is derived from personal experience[ Copyright Þ 1999 John Wiley + Sons\ Ltd[ KEY WORDS sub!additivity^pruning bias^de!biasing Likelihood can be expressed using a probability format "e[g[ 9[91#\ a relative frequency format "e[g[ 19 in every 0999\ one in 09\ 04)#\ or an absolute frequency format "e[g[ 0921 British schoolgirls 0 #[ This distinction between the format in which information about likelihood is expressed is quite separate from the distinction between single!event likelihood "associated with a unique outcome such as {Molly becomes a professional|# and distributional likelihood "associated with an outcome representative of a reference class or distribution such as {A girl drawn randomly from British schools becomes a pro! fessional|#[ However\ any description in frequency terms is likely to be distributional[ Distributional likelihoods can be derived from natural sampling or from systematic sampling "Gig! erenzer and Ho}rage\ 0888#[ Natural samples are those selected randomly from the general population[ For example\ we might pick 0999 humans at random[ The distribution of characteristics or events within this sample will be representative of the general population[ Systematic samples are those that have been size!matched and drawn randomly but from sub!populations[ For example\ we might draw 0999 people at random from those who are right!handed and 0999 at random from those who are left!