# Blinding vs. Double Blind in Statistics: Simple Definition, Examples

RCTs > Blinding in Statistics

## What is Blinding in Statistics?

In medical trials, the term blinding, or double-blind, usually refers to the practice of keeping patients in the dark as to whether they are receiving a placebo or not. It can also refer to allocation concealment, which is used to avoid selection bias.

### Double-blind vs. Blinding in Statistics

Blinding, or double-blinding, is when a patient does not know what treatment they are receiving. They could be getting either a placebo or the real drug. Blinding also refers to the practice of keeping the name of the treatment hidden. For example, patients might know they are involved in a trial for arthritis, but they will not know the name of the brand name in the trial. In “double-blind” trials, the clinical team— people involved in the patient’s management, the people collecting samples from the patient and the people analyzing the data also do not have any knowledge of whether the patient is getting the placebo or not.

Placebos can be used for blinding in statistics.

## Why Blinding in Statistics is Important

Blinding is especially important in subjective trials to avoid skewed results. For example, blinding would be used where pain relief is being studied. If a patient knows they are receiving a “real” drug, they will be more likely to report pain relief than those patients receiving a placebo. Blinding is less important in trials when there is a more objective criteria at stake, such as avoiding a patient’s death (for example, in cancer trials).

### Blinding in Statistics: Double Dummy Method

The double dummy method is used when a pair of medicines is being studied. For example, a combination drug for HIV treatment. In double dummy trials, there are two placebo drugs and two active drugs. The patients may be given a double placebo, or one placebo and one active drug of each type.

### Blinding in Statistics: Allocation Concealment

Allocation concealment is the practice of not revealing to a patient what treatment they are being allocated until they are actually enrolled in a trial, to avoid selection bias. In other words, a clinician might be tempted to ensure that certain patients (perhaps those in more pain) receive the “real” drug. The practice also keeps patients and the trial team unaware of upcoming assignments. Although the practice frequently annoys clinicians and their patients (see this NCBI article) allocation concealment ensures that random distribution of treatments is ensured.

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Statistical concepts explained visually - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.