Article Figures & Data
Figures
- Figure 1.
Which line is longer?
- Figure 2.
The Ebbinghaus illusion. Which pink circle is bigger?
- Figure 3.
Precision (reliability) and accuracy (validity).
- Figure 4.
Stages of the scientific process at which confirmation bias can influence the outcome.
- Figure 5.
Data from Table 2 (p. 67) of Rosenthal and Lawson 1964. Source code for figure: https://github.com/rickborn/eNeuroCB2024/blob/main/eNeuro_figure_code/Python/figure5_Born_eNeuro.ipynb
- Figure 6.
A, Binomial distribution, illustrating a problem of balancing group sizes with simple randomization. B, Method for block randomization. Panel B adapted from Figure 2 of Kang et al., 2008. Source code for panel A: https://github.com/rickborn/eNeuroCB2024/blob/main/eNeuro_figure_code/Python/figure6A_Born_eNeuro.ipynb
- Figure 7.
Binomial statistics. A, One example of a simulation of tossing a fair coin 100 times. B, Results from 2,000 such simulations. For each simulation, I recorded the number of “switches” (H followed by T or T followed by H) as well as the length of the longest run of consecutive tosses of the same value (H or T). C, The probability of runs of different lengths is described by the geometric distribution. Source code for figure: https://github.com/rickborn/eNeuroCB2024/blob/main/eNeuro_figure_code/Python/figure7_Born_eNeuro.ipynb
- Figure 8.
Flow chart to guide the choice of a method for randomization of subjects. Adapted from Figure 6 of Kang et al., 2008
- Figure 9.
Rigor icons for (A) randomization and (B) blinding. PNG files freely available from: https://www.ninds.nih.gov/current-research/trans-agency-activities/rigor-transparency/rigor-champions-and-opportunities/maximizing-data-transparency-rigor-icons
Tables
Allocation sequence # Animal ID# Group ID# Group identity Treatment administered 1 BL12SC7GM9 1 Rx Drug X 2 BL12SC3GM8 1 Rx Drug X 3 BL12SC8GM4 2 Ctrl Normal saline 4 BL12SC5GM8 2 Ctrl Normal saline 5 BL12SC7GM5 2 Ctrl Normal saline . . . 20 BL12SC7GM6 1 Rx Drug X What to do Who does it Comments 1 Generate a random allocation sequence Colleague Figure 6B 2 Shuffle all animal ID#s Colleague 3 Assign shuffled ID#s to allocation table Colleague Table 1 4 Generate group ID#s Colleague For analysis, e.g., Rx = 1, Ctrl = 2 5 Prepare syringes of substances to be administered Experimenter Don't label individual syringes! Group in separate beakers (X/Ctrl) 6 Label each syringe with an animal ID# Colleague Refer to allocation table 7 Collect data, referenced to animal ID# Experimenter 8 Provide analyst with group ID#s Colleague Statistical comparison of groups 9 Reveal group identity after analysis is complete Colleague - Table 3.
Frequency of blinding and randomization in various studies of animal disease models
Disease modeled Number of papers Blind outcome assessment, # % [95% CI] Randomized group allocation, # % [95% CI] Alzheimer's disease 428 95 22 [18, 26] 67 16 [12, 19] Multiple sclerosis 1,117 178 16 [14, 18] 106 9 [8, 11] Parkinson's disease 252 38 15 [11, 20] 40 16 [12, 21] Intracerebral bleed 88 43 49 [38, 60] 27 31 [21, 41] Focal ischemia 260 87 33 [28, 40] 100 38 [32, 45] Totals 2,145 441 21 [19, 22] 340 16 [14, 17] Data from Table 1 of Sena et al., 2014.
In this issue
Jump to section
- Article
- Abstract
- Significance Statement
- Our Biased Brains
- Confirmation Bias
- Confirmation Bias Distorts Observations
- Mitigating Confirmation Bias through Randomization and Blinding
- Randomization
- Blinding
- Does Blinding Make a Difference?
- How Good Is Your Mask?
- Are Anti-CB Design Features Widely Used?
- Tools for Removing Subjectivity
- Objective Assessment of Behavior
- Say What You Did!
- Summary and Conclusions
- Data Availability
- Footnotes
- References
- Synthesis
- Figures & Data
- Info & Metrics
- eLetters