Blinding and placebo
Definition taught in Studies, data, and evidence, part of Data analysis
Blinding and placebo
| Type | Definition |
| Taught in | Studies, data, and evidence |
| Part | Data analysis |
| Worked examples | 1 |
| Questions using it | 2 |
In a blind experiment the units do not know which treatment they received. In a double-blind experiment the people who give the treatments and measure the responses do not know either. A placebo is a control treatment that resembles the active one in every manageable way but lacks the active ingredient.
Examples
Alice’s caffeine experiment
Worked example from Studies, data, and evidence
Alice asks whether caffeinated cola raises the heart rate more than decaffeinated cola. She measures the heart rates of 20 friends, gives 10 of them 250 mL of caffeinated diet cola and 10 caffeine-free diet cola, waits half an hour, and measures again. The changes in beats per minute are
| Caffeinated | 17 | 22 | 21 | 16 | 6 | −2 | 27 | 15 | 16 | 20 |
| Decaf | 4 | 10 | 7 | −9 | 5 | 4 | 5 | 7 | 6 | 12 |
Question. Identify the response, the explanatory variable and the design choices, and summarise the data.
Solution. The experimental units are the 20 friends. The response is the change in heart rate, and the explanatory variable is caffeine, a factor with two levels. Several choices protect the comparison. Diet cola is compared with decaf diet cola because the two differ only in caffeine; regular and decaf regular cola also differ in sugar, energy and sodium. Half an hour is roughly the time caffeine takes to be absorbed. Choosing the 10 caffeine drinkers at random, rather than as, say, the 10 women, prevents a difference in heart rate from being a difference between the groups. Not telling the friends which drink they had, and ideally not telling whoever reads the pulse meter, prevents expectations from moving the response. Using friends at all limits the conclusion to people like Alice’s friends.
The decaf group’s mean change is 5.1 beats per minute and the caffeinated group’s is 15.8, a difference of 10.7. In a spreadsheet the data should be in standard form, one row per friend, with a column for the change and a column for the caffeine level:
alice <- data.frame(
pb = c(17, 22, 21, 16, 6, -2, 27, 15, 16, 20, 4, 10, 7, -9, 5, 4, 5, 7, 6, 12),
Caffeine = rep(c("Yes", "No"), each = 10)
)
tapply(alice$pb, alice$Caffeine, mean)Whether a difference of 10.7 could easily arise by chance, with no effect of caffeine, is the question of the two-sample t test, worked in Testing examples.
Used in
- Taught in Studies, data, and evidence, where the derivation and examples are.
- Also used in Analysis of variance examples
- Questions that use it (2) — Facial tumours in Tasmanian devils (answer) · A dose comparison for middle-of-the-night insomnia
- All 6 questions on Studies, data, and evidence
See also
Randomised controlled trial · Observational study · Designed experiment · Confounding variable · Simple random sample · Stratified and cluster sampling