88 Percent of Drivers Beat the Median, and Your Discipline Probably Does Too
Svenson (1981): most drivers rated themselves safer than average. What that and the Dunning-Kruger dispute mean for judging your own follow-through.
In this article7 sections
In Ola Svenson’s 1981 experiment, 88 percent of the American students and 77 percent of the Swedish students said they drove more safely than the others in the study. On driving skill, 93 percent of the Americans and 69 percent of the Swedes put themselves above average. Across 161 people, that is a lot of drivers claiming a spot in the better half. Follow-through gets the same generous grading: most people who are asked will place their own discipline above the typical person’s, which cannot be true of a majority, and the verdict “I’m basically disciplined, I just had a bad week” is the one least likely to be checked. A record you did not write from memory is the cheapest correction.
What did Svenson actually find?
The paper, “Are we all less risky and more skillful than our fellow drivers?”, appeared in Acta Psychologica (volume 47, pages 143-148). It had 81 American and 80 Swedish students rate their own driving against the other drivers in the experiment. The figures above are the percentages who put themselves in the top half, on safety and on skill separately. The skill result differed most between countries (93 versus 69 percent), a reminder that the size of the effect depends on the group and the question. A successful replication appeared in 2023 (Koppel and colleagues).
Why is that arithmetic impossible?
If the comparison is against the group’s own median, at most half the group can sit above it. Somewhere near 80 or 90 percent cannot. One caveat that critics of popular retellings raise: if people compare themselves with the mean instead of the median, and the distribution has a long tail of very bad drivers, a majority can legitimately beat the mean. That is why careful versions of the finding use ranks or the median, and why the claim here is about the pattern across many studies and traits, not about any single percentage.
Why would discipline attract the same bias?
Mark Alicke (1985, Journal of Personality and Social Psychology) studied which traits people rate themselves above average on. The effect was strongest for traits that are both desirable and seen as under one’s own control. Follow-through fits that description well: it is good, and it feels like a choice. None of the studies cited here measures better-than-average ratings of wake-up reliability or workout consistency directly, so the application to discipline is an inference from the general effect, not a measured result.
There is also a reason the verdict survives unchecked. The evidence you would use is your own memory of recent weeks, and memory keeps the intentions and the best days. The research on why goals take longer than planned covers the forward-looking half of this optimism, and scoring your goals as probabilities tests whether your predictions hold up. This post is about the backward-looking half, the grade you give your own past.
What about the Dunning-Kruger effect?
Justin Kruger and David Dunning’s 1999 paper, “Unskilled and unaware of it” (Journal of Personality and Social Psychology, 77, 1121-1134), tested Cornell students on humor, logical reasoning and grammar. People in the bottom quartile of actual scores estimated their standing far above where it was, and people in the top quartile tended to underestimate theirs slightly.
The popular version, that incompetent people are supremely confident, goes further than the data. Nuhfer and colleagues (2016, Numeracy) showed that random numbers can produce the same graph, because plotting self-estimates against actual scores builds in regression to the mean plus a general better-than-average tilt. Gignac and Zajenkowski (2020, Intelligence, 80, 101449) argued the effect is mostly a statistical artefact. Others disputed that analysis, including a 2023 response in the same journal, and the argument is still open.
What both sides seem to accept is narrower and more useful here: self-ratings tend to run high, and they track real performance only loosely. Whether the lowest performers are uniquely blind to it is the contested part. You do not need to settle it to act on the first part.
What does the gap look like in a 28-day log?
This is an illustration with invented numbers, not data from a study or from a user. Suppose someone says they are up on time “most days” and, asked to put a number on it, says five mornings out of seven. Over 28 days that is about 20 mornings. “On time” is defined in advance as out of bed within 10 minutes of the alarm, and a timestamped log records this:
| Week | On-time mornings (of 7) |
|---|---|
| 1 | 5 |
| 2 | 3 |
| 3 | 2 |
| 4 | 3 |
| Total | 13 of 28 (46%) |
Recall said 20 of 28 (71%). The record says 13 (46%). The gap is seven mornings. Notice what produced the recalled figure: week 1 was good, so it anchors the story, and the weak weeks were each explained away at the time (“travel,” “bad night”) so none of them registered as a pattern. Each excuse may have been true. Four weeks of true excuses is still 15 missed mornings.
What counts as a record?
The corrective has to be something you did not write afterward from memory, because memory is the instrument that gave the inflated answer. Timestamps, photos and video made at the moment qualify. A journal entry made at 9 p.m. summarizing the morning is memory with better handwriting. A record that other people can see has a further advantage: you cannot quietly redefine “on time” when someone else can see the clip.
DontSnooze, an iOS app, produces this kind of record as part of waking up. When the alarm fires you have 30 seconds to film proof that you are up. If you miss it, a random photo from your camera roll goes to your friends in the app and stays up for 48 hours, with screenshots disabled. Watching friends is free, and posting your own proof and streaks needs Pro. There is more on the mechanics in how DontSnooze works, and why video proof outperforms self-report covers the evidence-hierarchy case in detail.
Whether you use an app, a spreadsheet filled in at the moment, or timestamps from a phone, the useful test is to guess your number first, write it down, then compare it with the record.
If the log shows you keep outside promises better than your own, Rubin’s Obliger pattern is one framework for what to do next.
Two loose ends
Does everyone overrate themselves? No. Kruger (1999, Journal of Personality and Social Psychology) found the pattern reverses on tasks people find hard, such as juggling or computer programming, where many rated themselves below average. Self-ratings follow how difficult a task feels, so a person who finds early mornings brutal may underrate, while a person who finds “being disciplined” easy to picture will tend to overrate.
Is 28 days enough to see the gap in my own record? It is enough to rule out some stories, though not to pin down a rate. In the illustration above, 13 on-time mornings out of 28 gives a 95 percent confidence interval of roughly 30 to 64 percent (Wilson method). A recalled 71 percent sits outside that range, but 46 percent versus 40 percent would not be distinguishable. Longer logs narrow the interval.