If a clinical research trial shows that one intervention has better outcomes than another intervention, and the difference is statistically significant, that’s worth paying attention to. And if the same result is found in multiple independent studies, it’s important to take that into account when making treatment recommendations.
What it doesn’t mean is that the first intervention will necessarily be better for everyone. Within any treatment condition, there will be a spread of where individual results lie. It’s more when we look at the patterns we can start to draw conclusions about one treatment compared with the other.
But we have a long history of conflating ‘as a group’ with ‘in every case’. The law of averages becomes lore, and the treatment becomes the holy grail of evidenced-based practice.
To be fair, N = 1 is not a good clinical trial – there’s only so much we can draw from looking at the experience of one person. But N = 1 is a person. Not everyone will respond the same, and some people will have insignificant or adverse reactions to an intervention that works quite well for a lot of other people.
Sometimes research will miss important mediating variables that, if we knew them, might make us read the findings differently or in a more nuanced way. Some variables are easier to measure, giving particular interventions an advantage when they are being investigated. Just as clients have individual differences, so do we. The same treatment delivered by different clinicians does not get the same results.
We need to consider the possibility that this person would have been an outlier in the research we are relying on. It’s not them being non compliant or resistant, or us being incompetent, it’s just not a good fit for them. We need to stay open and curious so that we can work together to discover what will work for each person.
Helen Mentha 