Correlation Is Not Causation — But It's a Start

You've heard it a thousand times: correlation is not causation. It's the first thing anyone says when you mention that your sleep improved after starting magnesium. And they're right — technically. But that caveat, applied too broadly, can paralyse you into never learning anything from your own experience.

The truth is more nuanced. Personal correlations aren't proof, but they're the best compass most of us have for navigating supplement decisions. Used correctly, they're far more actionable than the population-level studies we usually rely on.

The limits of clinical evidence

Randomised controlled trials are the gold standard of medical evidence, and for good reason. They control for confounding variables, use large sample sizes, and measure specific outcomes rigorously.

But they have a fundamental limitation for personal supplement decisions: they measure averages. A study might show that magnesium glycinate improves sleep quality by 12% across 200 participants. That's useful information. But it tells you nothing about whether you are in the group that improved, the group that saw no change, or the small group that actually slept worse.

Population data gives you a reasonable prior — a starting point. It doesn't give you a personal answer.

What your own data can tell you

When you track your supplement intake alongside subjective outcomes — sleep quality, energy, mood, recovery — over 30 or more days, something interesting happens. Patterns emerge that are specific to your biology, your lifestyle, and your particular combination of supplements.

These patterns are correlations, not controlled experiments. They can't tell you why something is happening. But they can tell you that something appears to be happening, which is often the more useful question.

Consider a few examples:

• You notice that your sleep scores are consistently 15% higher on days you take magnesium before bed
• Your energy ratings dip on weeks when you skip your B-complex
• Your mood scores show no relationship to the expensive adaptogen blend you've been taking for three months

None of these are proof. All of them are actionable. The first two suggest something worth continuing. The third suggests something worth dropping — or at least testing more deliberately.

The correlation engine

Stack Almanac's correlation engine works by continuously analysing the relationship between your supplement patterns and your logged outcomes. As your dataset grows, the statistical confidence in each correlation increases.

This isn't about generating a single dramatic insight. It's about gradually building a clearer picture of what your body responds to. After 30 days, you might see tentative signals. After 90, those signals either strengthen into reliable patterns or fade into noise — both useful outcomes.

The key principle: your data doesn't need to be perfect. It needs to be consistent. A rough sleep score logged every day for two months is infinitely more useful than a precise sleep score logged sporadically for six.

From correlation to protocol

The practical workflow is straightforward. Track consistently. Review your correlations. Form a hypothesis — "magnesium seems to help my sleep." Then test it deliberately: drop the supplement for two weeks and see if the pattern holds.

This is the scientific method applied to a sample size of one. It won't get published in a journal, but it might save you hundreds of pounds a year on supplements that aren't doing anything for you.

Correlation isn't causation. But when it's your correlation, tracked consistently over time, it's the most honest data you have.

[Start surfacing your personal correlations with Stack Almanac.](https://stackalmanac.com)