10.2 Blinding

Blind analysis has emerged as the newest addition to the scientific process to guard against scientists' own confirmation bias, especially in high-precision experiments involving complex analyses, by preventing the scientists from seeing the result of their analysis as they refine and debug the analysis procedure. We illustrate blinding techniques and their dramatic effect through a simple measurement experiment.

The Lesson in Context

This lesson offers solutions to potential pitfalls in scientific studies raised in 10.1 Confirmation Bias and 4.2 Finding Patterns in Random Noise. We use a stick measurement activity to illustrate the effects of confirmation bias and to motivate techniques to reduce its effect, especially blind analysis. These techniques are not universally employed in all fields of science today, and students pursuing a scientific career are encouraged to introduce these techniques in their own work.

Earlier Lessons

4.2 Finding Patterns in Random Noise

Blind analysis is one way to prevent some forms of [math]\displaystyle{ p }[/math]-hacking. For example, choices to be made about a study, such as the exact statement of the hypothesis, definitions of terms, and statistical techniques, can be preregistered or made "blinded" to the data. The effects on the final result due to these choices may be hidden from the researcher during analysis. These techniques prevent motivated reasoning during analysis decisions.

10.1 Confirmation Bias

Blind analysis helps prevent scientists from the temptation to make choices that make the result more likely to confirm the researcher's own prediction or to match currently accepted knowledge. Otherwise, non-confirming, surprising results may be incorrectly missed.

Later Lessons

13.1 Denver Bullet Study

In group decision making, when factual evaluation and values evaluation are made by two different groups of people without knowledge of the other, it prevents evaluation motivated by the need to confirm a personal belief.

Takeaways

After this lesson, students should

Recognize what types of blinding are useful for solving what types of errors.
Be able to explain why blind analysis might be needed, by explaining the errors that can arise in its absence.
Recognize when blind analysis is being used and explain what function it serves. Identify situations and decisions in which blind analysis would be useful.
Be able to evaluate techniques (e.g., registered replication, adversarial collaboration, peer review)
1. for ability to address confirmation bias, and
2. in comparison to blind analysis.
Propose how to use blind analysis for simple studies.

Blind Analysis

Making all decisions regarding data analysis before the results of interest are unveiled, such that expectations about the results do not bias the analysis. Usually co-occurs with a commitment to publicize the results however they turn out.

Double Blind

Studies where both the participants and the administrators of the experiment are blinded as to whether they're in the control or intervention group.

Preregistration

A research group publicly commits to a specific set of methods and analyses before they conduct their research.

Registered Replication

One or more research groups commit to a specific set of methods and procedures to replicate earlier work to see if they get the same results (typically with the input of the original research team). Results are publicized regardless of outcome.

Registered Reports

When studies are peer reviewed and journals commit to publishing before the research is undertaken. This reduces publication biases where journals prioritize interesting or statistically significant findings over null results.

Adversarial Collaboration

Scientists with opposing views agree to all the details of how data should be gathered and analyzed before any of the results are known.

Peer Review

New results are evaluated by other experts in the same field to determine whether they are valid. This only reduces confirmation bias insofar as reviewers don't share the same biases.

Muon [math]\displaystyle{ g }[/math]−2 Experiment

This experiment performed highly precise measurements of the magnetic dipole moment of muons to test the theoretical predictions of the currently accepted model of elementary particles. Blinding is done by injecting a secret code into all of the data that would undergo analysis, so that the scientists involved would not make specific choices in the analysis in a way that makes the final value agree with the theoretical prediction. The secret code was kept in a physical locker, the opening of which was highly publicized in the announcement event. Once the data was "unscrambled", the result shows that there is indeed a sizeable deviation of the measured value from the theoretical prediction.

Why is the Muon [math]\displaystyle{ g }[/math]−2 Experiment Shifting Time?

A short video about this process.

[math]\displaystyle{ p }[/math]-hacking and Preregistration

[math]\displaystyle{ p }[/math]-hacking: A Demonstration

A demonstration of this type of [math]\displaystyle{ p }[/math]-hacking.

Blind analysis is just another name for a double blind experiment.

These concepts are related but distinct. A double blind experiment keeps both participants and administrators from knowing who is in the control or intervention group, primarily in treatment testing with a placebo. Blind analysis instead applies to the analysis process once the data has been collected, hiding the results of interest while analysis decisions are made. Blind analysis may be employed whether or not double blinding is.

After this lesson, students should

Attitudes
1. One should always be looking for ways that we get things wrong (by fooling ourselves or due to bugs in our reasoning processes) so that we can invent better procedures.
  1. This is important both for methods long in use and new ones (e.g. big data).
Concept Acquisition
1. Blind analysis: Making all decisions regarding data analysis before the results of interest are unveiled, such that expectations about the results do not bias the analysis. Usually co-occurs with a commitment to publicize the results however they turn out.
2. Examples of analysis decisions for which blind analysis could be useful:
  1. Stopping rules for when to stop looking for flaws in your experimental design or for computer/math bugs.
  2. Data selection decisions.
  3. Decisions about which analysis procedures to use (e.g. grouping decisions).
3. Confirmation bias drives the need for blind analysis.
4. Confirmation bias is pervasive and doesn't necessarily indicate any fraudulent activity.
5. Approaches to reducing confirmation bias other than blind analysis:
  1. Preregistration: A research group publicly commits to a specific set of methods and analyses before they conduct their research.
  2. Registered replication: A research group (or groups) commits to a specific set of methods and procedures to verify the result of an earlier work (typically with the input of the original research team). Results are publicized regardless of outcome.
  3. Adversarial collaboration: Scientists with opposing views agree to all the details of how data should be gathered and analyzed before any of the results are known.
  4. Peer review: New results are evaluated by other experts in the same field to determine whether they are valid. This only reduces confirmation bias if reviewers don't share biases.
6. Scientists are constantly looking for bugs in scientific practices in order to fix them. Blind analysis is just the latest example of scientists recognizing a bug in their practice (e.g., a way of being fooled) and adjusting practice to account for/remove the bug.
Concept Application
1. Explain why blind analysis might be needed, by explaining the errors that might arise in its absence.
2. Recognize when blind analysis is being used and explain what function it serves. Identify situations and decisions that would call for blind analysis.
3. Evaluate techniques (e.g., registered replication, adversarial collaboration, peer review)
  1. for ability to address confirmation bias, and
  2. in comparison to blind analysis.
4. Propose how to use blind analysis for simple studies.
5. Stretch Goal: Propose new practices to solve a new (fictional) problem with current scientific practice. (This is a stretch goal for evaluation, too! Can we invent a new problem with a new scientific practice?)

Additional Content

You must be logged in to see this content.