Why raw numbers can be misleading
Imagine you read: "Group A accounts for 60% of all tribunal cases!"
This sounds alarming. But is it? That depends entirely on how many people are in Group A to begin with.
Key insight: If Group A makes up 75% of the workforce but only 60% of tribunal cases, they're actually underrepresented in tribunals โ the opposite of what the headline implies.
Let's make this concrete. Here's a workforce of 20 doctors โ 15 from Group A (teal) and 5 from Group B (red):
Now, let's say 4 doctors face tribunals. Here's Scenario 1 โ proportional representation:
Group A has more cases (3 vs 1), but that's expected โ there are more of them. Now here's Scenario 2:
Same number of cases (2 each), very different meaning. Group B has the same absolute count as Group A, but they're only 25% of the workforce โ so they're appearing at twice the expected rate.
Here's the clearest way to think about it:
If you picked one random person from each group, who is more likely to face a tribunal?
A randomly selected person from Group B is 3 times more likely to face a tribunal than a randomly selected person from Group A โ even though they have the same number of tribunal cases.
We calculate the representation ratio like this:
Tribunal: 50% (2 of 4 cases)
Workforce: 75% (15 of 20 doctors)
Ratio = 50% รท 75% = 0.67x
Underrepresented
Tribunal: 50% (2 of 4 cases)
Workforce: 25% (5 of 20 doctors)
Ratio = 50% รท 25% = 2.0x
Overrepresented (2x)
In our MPTS data, we compare tribunal appearance rates against the GMC workforce baseline โ the actual ethnic composition of UK doctors.
For example:
This is why we show both views: The raw numbers tell you the scale. The representation ratio tells you if there's a disparity worth investigating.
Toggle "Show Over/Under Representation" on the dashboard to see both perspectives.
Representation ratios reveal patterns, not causes. Overrepresentation could result from:
Correlation is not causation. This data identifies disparities that warrant further investigation โ it does not prove discrimination or exonerate any group.