The ‘#PEDroTacklesBarriers to evidence-based physiotherapy’ campaign will help you to tackle the four biggest barriers to evidence-based physiotherapy – lack of time, language, lack of access, and lack of statistical skills.

If you are new to the campaign, we suggest that you start at the beginning by looking at earlier posts on strategies to tackle the barriers of lack of time, language and access. These are available on the campaign webpage, blog, Twitter (@PEDro_database) or Facebook (@PhysiotherapyEvidenceDatabase.PEDro).

A lack of statistical skills is a common barrier to interpreting evidence and implementing evidence-based physiotherapy. Last month, the #PEDroTacklesBarriers campaign focused on understanding intention to treat analysis. This month, we focus on understanding confidence intervals with three clinician-researchers.

	Aidan Cashin, Exercise Physiologist and researcher, University of New South Wales, Australia Area of practice: Comparative effectiveness of interventions for people with chronic pain
	Kate Scrivener, Physiotherapist, educator and researcher, Macquarie University, Australia Area of practice: Post-stroke physiotherapy intervention and research.
	Mark Elkins, Scientific Editor of Journal of Physiotherapy Area of practice: Physical and pharmacological therapies in respiratory disease and improving the understanding and application of published research by clinicians.

How precise is the reported effect of an intervention in a trial for my patient?

The point of studies that compare the effects of treatments is to give readers an idea about what would happen if a patient received one treatment versus another. The study does this by producing an ‘effect estimate’. For continuous measures this is the between-group difference; the mean outcome score for the intervention group minus the mean outcome score for the control group. Note that we are not talking about p-values here, for a range of reasons p-values are not useful for informing treatment decisions.

It is important to recognise though that the effect in the study comes from a study sample. One implication of this is that the best the researchers can do is provide an estimate of the effect in the whole population. All estimates are imprecise, and it matters how imprecise they might be. The most important and useful tool researchers have to describe the precision of an effect estimate is the confidence interval.

Confidence intervals are often misinterpreted. They do not represent the range of effects that 95% of patients will experience, or the largest and smallest effects an individual patient can expect.

The technical explanation of a confidence interval is quite complicated but there is a way to interpret them that is close enough for clinical purposes. The confidence interval is the range of values that the population effect most likely falls into. So, if a trial has a mean between-group difference of 2 points, with a confidence interval from 1 to 3, then the best estimate of the treatment effect is 2 points, but it could be anywhere from 1 point to 3 points.

For a clinician, the range of plausible effects (values within the confidence interval) can form part of the discussion with a patient about treatment options in coming to a shared decision.