Relative Risk vs Absolute Risk
A personal essay, not an evidence assessment. Our graded assessments of individual ingredients — written to a published standard, from full texts — are in the evidence section. Nothing here is a statement about what any product does.
Numbers can be completely accurate and still create the wrong impression.
One of the easiest ways this happens is through relative change.
Imagine that an event occurs in:
2 people out of 100 and an intervention reduces it to:
1 person out of 100.
The relative reduction is:
50%.
The absolute reduction is:
1 percentage point.
Both are true.
They do not feel remotely the same.
Relative numbers are emotionally powerful
“50% reduction” sounds substantial.
“One fewer person per hundred” sounds different.
Neither formulation should automatically replace the other.
The responsible approach is usually to understand both.
This matters especially whenever baseline probability is small.
A large relative change can represent a very small absolute difference.
The same principle applies beyond medical risk
Imagine a cognitive test.
Performance moves from:
100 points to:
102 points.
We might describe that as:
2% improvement.
That may be meaningful.
It may be trivial.
It depends on the scale, variability, measurement reliability and practical consequences.
Percentages do not contain context automatically.
Baseline matters
Suppose a supplement reduces a symptom from:
40% to 20%.
Again:
50% relative reduction.
But this time:
20 percentage points absolute.
The same relative percentage now represents something potentially much larger in practice.
This is why I dislike dramatic percentages appearing without their baseline.
I want to see the raw numbers
Whenever possible:
How many participants?
How many events?
What was the baseline?
What was the final value?
What is the absolute difference?
What is the relative difference?
The reader should not need to reverse-engineer the impressive percentage.
Relative risk is not dishonest
This deserves saying clearly.
Relative measures are scientifically useful.
They make comparisons possible.
They are standard analytical tools.
The problem begins when one metric is chosen because it sounds more impressive to a consumer.
If both numbers are available, show both
That is the standard I would like MindHeaven to follow.
Not because consumers need a statistics lesson every time they read a page.
Because context is part of the result.
A number without its denominator can tell a technically correct story that is practically misleading.
And I have little interest in being technically correct while leaving the reader with the wrong understanding.