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The bias that results from the selection of non-random data for analysis. Can be detected by ensuring the randomization and representation in samples. An example is media outlets favoring stories that resonate with their perceived audience.

Occurs when the outcome of an experiment or research influences the decision to publish it. Detected by reviewing the 'file drawer' problem or using funnel plots. An example is news channels preferring to report sensational stories over mundane but important issues.

A form of bias where the opinion of an authority figure is accepted as the truth without critical thought. Detected through skeptical scrutiny of sources. For example, journalists may not question a government official's statement due to their status.

A tendency to search for, interpret, and recall information in a way that confirms one's preconceptions. Detected by challenging underlying assumptions and seeking out contrary information. Examples include favoring news that aligns with one's political beliefs.

Occurs when the media presents an issue as being more balanced between opposing viewpoints than the evidence supports. Detected by evaluating the weight of evidence on each side. Examples include equal coverage to climate change skeptics as to the vast majority of scientists who acknowledge it.

The bias introduced by presenting a topic in a certain way or context that influences perception. Detected by observing the language and context used around issues. Examples include describing a tax cut as 'relief' to suggest benevolence.

Media content influenced by the profit motives of the publishers. Detected by analyzing ownership and funding sources. Examples include clickbait articles and ad-driven content.

Preference for the current state of affairs, leading media to disproportionately cover existing power structures favorably. Detected by reflecting on the diversity of viewpoints presented. Examples are underreporting social change movements or minority perspectives.