Bias in Research: Types, Examples and How to Reduce It

Home › Research Methodology › Bias in Research

Below, we explain the main types of bias in plain language and look for them honestly in ScholarsColab’s own data.

Common types of bias at a glance

Example: looking for bias in ScholarsColab’s data

To explain bias, we check our own data for it, the way a careful reviewer would. Our data: 41,792 papers, mostly published between 1998 and 2026, by 967 ScholarsColab mentors (data updated 15 October 2026).

Step 1
Selection bias: who is in the data? Our data comes from researchers who chose to join ScholarsColab, not a random sample of all researchers.

Engineering & computer science alone accounts for 49.8% of the papers, so this group of researchers does not mirror research as a whole. Any result about “researchers in general” would lean towards engineering habits.

Step 2
Measurement bias: what does our measure miss? We detect a paper’s method from its title. Only 7,794 of the 41,792 papers (18.6%) name a recognisable method or design in their title; the rest cannot be classified from the title alone. Methods that are rarely named in titles are therefore undercounted every time, which is a systematic error, not a random one.

Step 3
Confounding: what else could distort a comparison? Older papers have had more time to be cited. Papers from 2010–2014 have a median of 8 citations, while papers from 2025–2026 have 0. Newer papers have simply had less time to be cited. If we compared old reviews with new research papers, age would bias the result, so we compare papers from the same years.

Step 4
State what remains. We cannot remove every bias, so we describe the remaining limits on every page. That transparency is what makes research trustworthy.

How to reduce bias in your study

1. Use random or stratified sampling where possible, and describe who was not included.
2. Use validated, calibrated instruments and measure every group the same way.
3. Blind participants and assessors to group membership where you can.
4. Control for confounders through randomisation, matching or statistical adjustment.
5. Pre-register your plan and report all results, including non-significant ones.
6. Assess risk of bias with standard tools when you review other studies.

Common mistakes reviewers point out

• No discussion of possible biases in the limitations section.
• Convenience samples presented as representative.
• Unblinded outcome assessment in experiments.
• Selective reporting of only the results that support the hypothesis.

Frequently asked questions

Use this data in your own work

More research methodology guides

• Independent vs Dependent Variables
• Control Group vs Experimental Group
• Mediator vs Moderator Variables
• Validity vs Reliability (and Generalizability) in Research, with Real Data
• Retrospective vs Prospective Studies
• Data Collection Methods in Research
• Types of Study Design
• Research Hypothesis
• Literature Review vs Systematic Review vs Meta-Analysis, with Real Data
• Sampling Methods in Research
• Experimental vs Quasi-Experimental Design
• Correlation vs Causation
• Deductive vs Inductive Reasoning in Research, with Real Examples
• Incidence vs Prevalence
• Descriptive vs Inferential Statistics, Explained with Real Data
• P-Value and Statistical Significance
• Operationalization
• Surveys and Questionnaires in Research
• Qualitative Research Methods
• Research Gap, Problem Statement and Research Question, Explained with an Example
• Pearson Correlation Coefficient (Pearson’s r)
• Chi-Square Test Explained
• ANOVA and the Kruskal-Wallis Test, Explained with Real Data
• Regression Analysis Explained
• Standard Deviation and Variance
• Post Hoc Tests Explained
• Confidence Intervals Explained
• t-Test vs Mann-Whitney U Test
• Effect Size Explained
• Heteroskedasticity Explained