Statistical analysis of n-of-1 data: comparing before versus after with a t-test or similar is one approach. Plotting the data visually is another. Both are valid.
It is one reading of the data and not the only reasonable one.
This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
Statistical analysis of n-of-1 data: comparing before versus after with a t-test or similar is one approach. Plotting the data visually is another. Both are valid.
It is one reading of the data and not the only reasonable one.
Confirming post #30 from a second method, which matters more than confirming it from a second person.
Record everything that changed, not just the intervention. Personal experiments are confounded by life, and the confounders are only recoverable if they were written down.
Adding a data point of agreement rather than a data point.
Post #34 is right about the mechanism and I think understates the practical bit.
Write down what you would accept as evidence that it did not work, before starting. That one sentence turns an anecdote into an experiment.
It is worth stating the boring hypothesis before the interesting one.
Taking post #38 at face value and following it one step further.
What would change my mind on Designing a personal experiment is a second dataset collected by someone with no stake in the first. Until then I hold it loosely and I would rather say so than pretend to more.
Post #36 and I disagree about the size of the effect, not about the direction.
The useful distinction on Designing a personal experiment is between what was measured and what was inferred from it. Both end up in the same sentence and only one of them has error bars.
I had written a reply contradicting post #37 and deleted it. Here is what survived.
Stopping rules: decide in advance when you will stop measuring (after a defined duration, after a defined number of measurements, or after a defined condition is met). Not deciding in advance means stopping when the result satisfies you, which is bias.
One more caveat and then I will stop qualifying: the sample selected itself.
A photograph or a device reading is a harder record than a recollection, and where one is available it should be the record.
Take it as a starting point and not as a specification.
The interesting personal experiments are the ones where the answer was no. They are also the ones least likely to be written up.
Coming back to post #41, because the follow-up matters more than the original answer.
Nothing in a personal experiment is medical advice to anyone including yourself, and several people here have found that a prescriber will happily discuss a well-documented one.
One case, stated as one case.
Seconded. It reads as careful rather than confident, which is the right register.
Checked the Designing a personal experiment claim against the primary source this morning. It survives, with a narrower scope than the version quoted here. Posting the narrower scope.
Reporting rather than recommending, on Designing a personal experiment. What happened is above. Whether it should have is a different question and not one I am qualified to answer.
An n of one tells you about one person, which is the person you are most interested in. That is the whole value and it is not nothing.
Reporting the whole series rather than the interesting segment is the discipline that makes personal data worth reading. Selective reporting is the default without effort.
It is the sort of thing that seems obvious in retrospect and was not at the time.
The arithmetic in post #51 is right; the assumption feeding it is the part to check.
A definition problem is doing most of the work in this Designing a personal experiment discussion. Once the term is pinned down I suspect the disagreement mostly goes away and what is left is small.
Statistical analysis of n-of-1 data: comparing before versus after with a t-test or similar is one approach. Plotting the data visually is another. Both are valid.
Post #53 is the version of this I will quote in future. One addition.
Regression to the mean explains a great many personal experiments. People start when things are at their worst, and things at their worst tend to improve.
One of those cases where knowing the mechanism does not help the decision.
Where I part company with post #54, and it is a narrow parting.
I would be cautious about generalising from the Designing a personal experiment example above. It is a good example. It is one example.
Duration should be set by how long an effect would take to appear rather than by patience. Stopping early is the commonest reason a personal experiment answers nothing.
Designing a personal experiment that could actually change your mind: that is the standard for an n-of-1 design. An experiment designed so that any result confirms what you already believed has not changed anything.
Written quickly, so the reasoning may be tighter than the wording.
Useful. I had the fact and not the reason, which turns out to be the important half.
Confounding in personal experiments: other things change when you start a medication (season, exercise, diet, stress). Documenting those confounders helps you understand their contribution to the result.