4.3.1: Research and Media Bias
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- 261266
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)We have begun to explore some examples of systemic bias such as algorithmic bias, gender bias in academic publishing, media bias in which Black women's work was omitted, and bias in how library materials are categorized. Other types of bias in the scholarly research process, and the media, as well as personal bias, also influence information ecosystems and how we interact with them.
Bias in the Research Process
We have already seen that gender and racial bias can influence which research studies are published. Bias can also influence how, and whether, research studies are conducted in the first place, as well as which studies get published based on their subjects rather than their quality. Bias can skew the presentation and interpretation of facts, leading to misinformation and a distorted understanding of a topic. Some examples of bias in the research process include:
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When a scientific study is funded by a group that supports a specific outcome for the research Example: A study on the effects of a particular medication is paid for by the drug company that produces that medication |
When a researcher sets up an experiment and chooses samples or groups that aren't representative of the population they are studying Example: A researcher is investigating the effects of a drug on the general population but does not include many women in the study |
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The tendency of research participants to see what they expect or want to see Example: A researcher who is sexist is observing women's behavior and interprets their behavior through the lens of their sexism |
When certain types of studies are published over others Examples: Racial bias leads a publisher to approve more white-authored papers than those from people of color; a publisher of a medical journal tends to reject studies about women's health in favor of studies about men's health. |
Media Bias
As we saw in Chapter 3, trust in previously-trusted sources of information has declined and facts themselves have become contentious (Head et al, 2020; Kavanagh & Rich, 2018; Mitchell et al, 2018; RAND, 2020). Experts debate how to assess and address the political polarization of media outlets. Groups such as Ad Fontes Media have categorized news media based on their level of reliability and where their political bias (Interactive media bias chart). Other experts have criticized this work, arguing that such categorizations of media outlets "portray the political center 'unbiased' [and implying that] the status quo power structure is the only system that can feasibly exist, and even the thought of alternative systems is seen as inherently radical" (Benjes-Small, 2021).
Regardless of political bias, other types of media bias can also distort how information is presented. Below are some examples.
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The public should be able to get information on all sides of an issue but that doesn't mean that all sides of the issue deserve equal weight. Media sources create false balance when they give equal airtime to both sides of an issue, even if 99% of experts agree with one side.
Image: Balanced reporting (1 of 2) by University of California Museum of Paleontology from Understanding science 101 has a CC BY-NC-SA 4.0 license. |
In untangling conflicting viewpoints in the media, it pays to investigate each person's area of expertise. For example, a PhD in physics does not make you an expert in medicine, or vice versa.
Image: Balanced reporting (2 of 2) by University of California Museum of Paleontology from Understanding science 101 has a CC BY-NC-SA 4.0 license. |
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Science and social science research is often presented in the news in very oversimplified ways. Omitting important details might misrepresent the findings of the original research.
Image: Elephant Africa nature by Thomas Zbinden from Pixabay Image: Line drawing of an elephant by Alvaro Montoro from Unsplash is in the Public Domain CC0 |
Correlation (two occurring at the same time) doesn't necessarily mean that one caused the other, but the media might imply a causal relationship when the original research shows no evidence of one.
Image: Per capita consumption of margarine correlates with the divorce rate in Maine by Tyler Vigen has a CC BY 4.0 license. |
Sources
Benjes-Small, C. (2021, Feb 23). Complex or clickbait?: The problematic Media Bias Chart. ACRLog.
Head, A.J., Fister, B., & MacMillan, M. (2020, Jan 15). Information literacy in the age of algorithms. Project Information Literacy. Licensed under CC BY-NC-SA 4.0
Interactive media bias chart. (n.d.) Ad Fontes Media. Accessed July 15, 2025.
Kavanagh, J. & Rich, M.D. (2018, Jan 16). Truth decay: An initial exploration of the diminishing role of facts and analysis in American public life. RAND.
Mitchell, A., Gottfried, J., Barthel, M., & Sumida, N. (2018, June 18). Distinguishing between factual and opinion statements in the news: The politically aware, digitally savvy and those more trusting of the news media fare better; republicans and democrats both Influenced by political appeal of statements. Pew Research Center.
RAND. (2018, May 16). How truth decay happens. YouTube.