Table 3. Bias in imaging experiments.

Type of bias	Examples in imaging experiments	Strategies
Selection bias	• Scanning samples for fields of view that “look good” or “worked” based on subjective or undefined criteria (also confirmation bias)	• Use microscope automation to select fields of view or scan the entire well
	• Choosing to image only the brightest cells/samples (e.g., highest expression level)	• Include all data in analysis, or determine criteria to discard a dataset before collecting data
	• Only including data from experiments that “worked” in analysis or publication
Confirmation bias	• Adjustments to the analysis strategy based on the direction the results are heading	• Validate the analysis strategy using known samples/controls ahead of time
	• Choosing analysis parameters that yield the desired or expected results, rather than choosing through validation with known samples	• Perform analysis blind
	• P-hacking (Head et al., 2015)
	• Choosing cells or parts of a sample that “make sense” based on the anticipated outcome
Observer bias/experimenter effects	• Spending more time focusing by eye (and therefore photobleaching) on one condition than the others	• Perform acquisition and analysis blind
	• Making subjective conclusions based on visual inspection of the image rather than making quantitative measurements	• Make conclusions based on quantitative measurements rather than qualitative visual impressions (measure length/width/aspect ratio, count, measure intensity, etc.)
Asymmetric attention bias/disconfirmation bias	• Performing image corrections only when result seems wrong or is not as expected	• Consider sources of error, validate, and apply corrections equally to all conditions and experiments

See Lazic (2016), Nuzzo (2015), Nickerson (1998), and Munafò et al. (2017) for more about bias and additional references.