Unit Lessons
(lessons marked with an * are included in the "Meeting" version of the unit)
Our biased brains
This lesson introduces the concept of confirmation bias, highlighting how our brains tend to favor information that reinforces existing beliefs. Learn why these biases exist from both psychological and neuroscientific perspectives, and how they can inadvertently affect decision-making in research.
Motive & Method
This lesson focuses on the pitfalls of designing experiments that only test a single, “favored” hypothesis. Learn about and practice developing multiple, mutually exclusive hypotheses to challenge assumptions and reveal hidden biases.
Researcher degrees of freedom
This lesson explains how subjective decisions, ranging from data cleaning to statistical analysis, can unintentionally favor an expected outcome. Learn how “researcher degrees of freedom” can lead to overconfident or skewed results.
Mitigating bias through masking
This lesson introduces the concept of masking (or blinding) in experimental design. Learn how masking can prevent both participants and researchers from inadvertently influencing results with examples from clinical trials.
How good is your mask?
In this lesson, the focus shifts to the evaluation of masking effectiveness. Learn about factors that may inadvertently reveal experimental conditions and how to assess whether masking has been compromised.
How good is your mask?
This lesson introduces differences between exploratory and confirmatory data analysis. Learn how blending these approaches without clear distinction can lead to confirmation bias and discuss strategies to preserve the integrity of research findings.
How good is your mask?
This lesson draws parallels between experimental masking and data leakage in machine learning. Learn about the concept of data leakage and its impact on model evaluation, emphasizing the importance of proper data partitioning.
How good is your mask?
This final lesson broadens the discussion to other biases that can influence research, including conformity bias and cognitive dissonance. Learn how to tie these concepts back to confirmation bias, discovering how multiple biases can compound and affect scientific outcomes.
Unit Overview
Randomization in experimental design is about systematically and thoughtfully imposing an order onto our treatment of variables. Specifically, randomization is a series of actions that reduce the interference of confounding variables and disturbance variables as you study causal relationships. Intentional action like this doesn’t feel very random, and that’s on purpose.
Activities
minutes
How do I know It’s real?
Randomization in experimental design is about systematically and thoughtfully imposing an order onto our treatment of variables. Specifically, randomization is a series of actions that reduce the interference of confounding variables and disturbance variables as you study causal relationships. Intentional action like this doesn’t feel very random, and that’s on purpose.
1
Activities
5
minutes