Is this a new bias? I haven’t seen it mentioned before. Abstract (emphasis mine):
Consumers routinely rely on forecasters to make predictions about uncertain events (e.g., sporting contests, stock fluctuations). The authors demonstrate that when forecasts are higher versus lower (e.g., a 70% vs. 30% chance of team A winning a game) consumers infer that the forecaster is more confident in her prediction, has conducted more in-depth analyses, and is more trustworthy. The prediction is also judged as more accurate. This occurs because forecasts are evaluated based on how well they predict the target event occurring (team A winning). Higher forecasts indicate greater likelihood of the target event occurring, and signal a confident analyst, while lower forecasts indicate lower likelihood and lower confidence in the target event occurring. But because, with lower forecasts, consumers still focus on the target event (and not its complement), lower confidence in the target event occurring is erroneously interpreted as the forecaster being less confident in her overall prediction (instead of more confident in the complementary event occurring—team A losing). The authors identify boundary conditions, generalize to other prediction formats, and demonstrate consequences.
Perhaps you could expand it and post to discussion, so it can be found by tags? I seem to remember a passage in SSC about good/poor calibration and high/low probabilities, in that recent post about internet communities, freedom and witch migration...
Is this a new bias? I haven’t seen it mentioned before. Abstract (emphasis mine):
Perhaps you could expand it and post to discussion, so it can be found by tags? I seem to remember a passage in SSC about good/poor calibration and high/low probabilities, in that recent post about internet communities, freedom and witch migration...
sounds like cross contamination of anchoring effects.
Although anchoring works with the presence of irrelevant numbers too.