The placebo effect is a phenomenon whereby the belief that you are seeking treatment for a specific condition might actually lead to the improvement of that condition... even if the treatment you're receiving is a sugar pill (i.e., a fake or "placebo" treatment).
The placebo effect happens all the time in clinical research, and in the real world. For reference, a 5-year study on finasteride found that 20% of men in the placebo group – despite taking a sugar pill to treat hair loss for the last 5 years – still showed no signs of further hair loss (with 5% showing signs of cosmetic improvement!).
For these reasons, the power of placebo creates a challenge when interpreting clinical studies on hair loss disorders. How can we know if anything works... when the power of belief can produce results even in the absence of any real hair loss treatment?
Strategies to Control for the Placebo Effect
One of the best ways to "parcel out" the placebo effect is through clinical trial designs. Specifically, we'd want to have a clinical study that is designed as:
- Robust (i.e., has a large-enough sample size of hundreds of people)
- Double-blinded (i.e., both the researchers and the participants don't know who's receiving the treatment)
- Controlled (preferably, by using two controls: a placebo group who thinks they're receiving treatment, and an "untreated" group who is doing nothing for their hair)
Having said that, studies like this are expensive – and not all investigation groups have access to the resources (finally or physically) to launch investigations this well-rounded, especially at the earliest stages of research.
Under these circumstances, when we cannot rely on well-designed clinical studies, we rely (instead) on study replicability. In other words, "Have we seen this study repeated by other research teams? And if so, were the results basically the same?"
If the answers are yes, and the number of repeat studies is high, that gives us better confidence (but not full confidence) that the placebo effect might not be driving all of those results. It may not necessarily rule out other factors of influence – such as the seasonality of the hair cycle – but it's a start in the right direction in building better trust between the quality of evidence and the outcomes presented in those research papers.