List of All Biases [Sorted by Popularity in Research Papers]

I analyzed the content of 98,709 randomly chosen research papers from PubMed to learn more about bias.

Specifically, I wanted to do 2 things:

  1. Rank 64 types of biases by popularity, in order to determine on which ones professional researchers focus the most in practice.
  2. Test the hypothesis that addressing bias issues is a sign of high-quality research.

Let’s first start with a brief summary of the results.

Here’s a summary of the key findings

1. Only 11% of medical research papers addressed the issue of bias (i.e. mentioned at least 1 type of bias).

2. The 10 most popular types of biases in research are:

  1. Confounding
  2. Selection Bias
  3. Recall Bias
  4. Reporting Bias
  5. Sampling Bias
  6. Information Bias
  7. Detection Bias
  8. Attrition Bias
  9. Ascertainment Bias
  10. Performance Bias

3. It is not enough to just mention the types of biases that may be present in your study, instead you should look for ways to eliminate, limit, or at least quantify the effect of bias on your results. In fact, our data suggest that just mentioning the type of bias is a statistically significant predictor of a lower-quality study!

64 different types of biases sorted by popularity

Out of the 98,709 research papers analyzed, only 10,811 (or 11%) mentioned at least 1 type of bias.

Here’s a table that summarizes these data:

RankBias TypeNumber of Articles
that Mentioned this Type of Bias
(Out of 10,811 Articles)
In Percent
1Confounding *644959.65%
2Selection Bias318029.41%
3Recall Bias110710.24%
4Reporting Bias5445.03%
5Sampling Bias4013.71%
6Information Bias2071.91%
7Detection Bias1931.79%
8Attrition Bias1761.63%
9Ascertainment Bias1281.18%
10Performance Bias1271.17%
11Hawthorne Effect1121.04%
12Misclassification Bias1121.04%
13Observer Bias1050.97%
14Confounding by Indication900.83%
15Referral Bias (a.k.a. Admission Rate Bias or Berkson’s Bias)860.80%
16Immortal Time Bias550.51%
17Language Bias550.51%
18Confirmation Bias500.46%
19Non-Response Bias370.34%
20Lead Time Bias310.29%
21Outcome Reporting Bias260.24%
22Verification Bias210.19%
23Volunteer Bias200.18%
24Allocation Bias180.17%
25Temporal Bias180.17%
26Collider Bias160.15%
27Availability Bias150.14%
28Perception Bias130.12%
29Incorporation Bias100.09%
30Spectrum Bias100.09%
31Funding Bias (a.k.a. Sponsorship Bias)90.08%
32Protopathic Bias60.06%
33Overconfidence Bias50.05%
34Length Time Bias30.03%
35Exclusion Bias20.02%
36Popularity Bias20.02%
37Lack of Blinding Bias10.01%
38Proxy Bias10.01%
39Inferential Bias10.01%
40Novelty Bias10.01%
41Unmasking Bias10.01%
42Chronological Bias10.01%
43Prevalence-Incidence Bias (a.k.a. Neyman’s Bias)00.00%
44Spin Bias00.00%
45Unacceptability Bias00.00%
46Previous Opinion Bias00.00%
47All’s Well Literature Bias00.00%
48Positive Results Bias00.00%
49Differential Reference Bias00.00%
50Apprehension Bias00.00%
51Centripetal Bias00.00%
52Compliance Bias00.00%
53Diagnostic Access Bias00.00%
54Diagnostic Momentum Bias00.00%
55Diagnostic Suspicion Bias00.00%
56Exposure Suspicion Bias00.00%
57Partial Reference Bias00.00%
58Hot Stuff Bias00.00%
59Informed Presence Bias00.00%
60Insensitive Measure Bias00.00%
61Mimicry Bias00.00%
62Non-Contemporaneous Control Bias00.00%
63One-Sided Reference Bias00.00%
64Wrong Sample Size Bias00.00%
* Although included in this table, it is debatable whether or not confounding should be considered a type of bias. (see Why Confounding is Not a Type of Bias)

Mentioning the type of bias in your study is not enough

In this section, we will be interested in whether or not addressing bias issues is a sign of high-quality research.

In order to answer this question, I used a linear regression model to study the influence of “mentioning at least 1 type of bias in a research paper” on “the Journal Impact Factor (JIF)” which is a good proxy for the quality of research — as higher-quality articles tend to be published in high JIF journals.

Here’s a summary of the linear regression results:

 CoefficientStandard Errorp-value
Intercept3.800.01< 0.001
Bias Mention
(Yes versus No)
-0.300.03< 0.001

The model shows that mentioning at least 1 type of bias in the study is associated with a 0.3 mean drop in the value of JIF (p < 0.001). Specifically, the average article that does not mention any type of bias is published in a journal with an impact factor of 3.8, compared to a JIF of 3.5 for the average article that does mention at least 1 type of bias.

Now it may be argued that the -0.3 difference is not practically significant, therefore at the very least, we can conclude that:

Just mentioning the type of bias that may affect your results does not make your study better. Instead, you should try tweaking your design, or adjusting your statistical analysis in order to control or limit the effect of that bias.


The list of biases used in this article is mostly based on along with other resources such as textbooks and research papers.

Further reading