So I have a sentence: Hence this option is free of abundance bias

I've made up the phrase "abundance bias" and I want to replace it with the proper scientific term. What is the exact term that would denote that the option will account for all subtleties and won't be biased towards stuff that happens to be more common?

Like for example, if a doctor sees 100 patients with X and someone who has Y (which has symptoms similar to X) comes in. The doctor fails to consider any subtleties that might make it different from X and just diagnoses X. This doctor has the "abundance bias" because the X was more common. You could say it's actually confirmation bias but that'll be when the doctor diagnoses based on his subjective experience and personal judgement rather than X being more widespread.

So, what is the right scientific term that would mean the same thing as "abundance bias"? Basically, the cognitive bias that arises when a particular thing is perceived to be much more common than the rest and hence it is considered to be the only case and the rest is disregarded/not considered

  • $\begingroup$ If there is a real need to use a scientific term (only needed when plain English is too long or ambiguous, or you want to highlight well-documented related effects), you are probably also looking for a specific paper to cite? $\endgroup$ – Steven Jeuris Jul 20 '18 at 8:03
  • $\begingroup$ Tbh not really. My research is actually statistical prediction and I wanted to present this phenomenon as a shortcoming of traditional methods. I couldn't fit a plain English explanation in my flow very well so I just wanted a concise phrase to describe it. I assumed this'd be a defined cognitive bias but turns out it's kinda not very well defined? Right now, the closest thing I've got is representativeness heuristic so I've modified my sentence to Hence this option is free of any subjective representativeness heuristic. $\endgroup$ – HMK Jul 20 '18 at 19:21
  • $\begingroup$ I've also been told saliency bias might be more appropriate, so I'm reading more about it to see if it that's so $\endgroup$ – HMK Jul 20 '18 at 19:23
  • $\begingroup$ In that case, I believe this question might be better suited for English Language & Usage SE. $\endgroup$ – Steven Jeuris Jul 20 '18 at 19:32
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    $\begingroup$ Yup, I posted there before I did here. That's where I got salience bias from $\endgroup$ – HMK Jul 20 '18 at 19:33

Wikipedia refers to "regressive bias" though I admit I have never heard the term used, defined as:

A certain state of mind wherein high values and high likelihoods are overestimated while low values and low likelihoods are underestimated

However, in addition to not recalling this term's use before, I also did not find it used in quite that context in the majority of results on Google Scholar for "regressive bias" - so I would use it only with caution (and probably not at all in the context you refer to) and along with your own definition if at all.

You could also possibly refer to the default effect although this mainly refers to a default action rather than a default conclusion, one could argue these are one and the same (i.e., if the default diagnosis for the symptom is X).

The availability heuristic is also relevant. Although this bias is normally focused on recency of exposure, surely the most common diagnoses would be most 'available' simply because it is encountered more often, whereas it is unlikely that the doctor has recently experienced disease Y. Of course, once they do encounter someone with Y, the availability heuristic would suggest that in the future they will be more likely to diagnose or at least consider Y when X is more likely based on simple probability.

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    $\begingroup$ The definition of regressive bias makes it seem it is exactly what I was looking for but seeing its usage I dont think that's the case. I think availability heuristic would be appropriate but I found representative heuristic on the regressive bias wiki which I think might be more descriptive of what I wanna convey. $\endgroup$ – HMK Jul 19 '18 at 23:01

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