Much to my surprise, liking rock and classical music predicts the same things about your personality: having greater “openness to experience” (a personality trait from the Big Five framework) and being more intellectual.

Makes sense for classical, but who would have guessed that’s true of rock?

Another surprise to me was that enjoying dance/electronic music, country music, and jazz music predicted similar traits: being more group-oriented (e.g., gravitating toward group rather than 1-1 interactions), being more extroverted, and being more spontaneous.

But each of these 3 groups also stood out uniquely. Enjoying country was associated with being more emotional, enjoying dance/electronic was associated with higher openness to experience, and enjoying jazz was associated with being less attention-seeking than the other two groups.

Enjoyment of both pop music and hip-hop was associated with being more emotional, but pop music enjoyers were more group-oriented, whereas hip-hop music enjoyers were more spontaneous.

All the correlations discussed here are between r=0.3 and r=0.45 in size, so they are moderately large. It would be neat to see whether this generalizes to non-U.S. samples.

You can explore all of these music genre correlations, plus over a million more correlations about humans, for free using PersonalityMap: https://personalitymap.io

This piece was first written on May 23, 2025, and first appeared on my website on May 29, 2025.

• created billion-dollar companies with huge influence (e.g., Elon Musk)
• made multiple revolutionary scientific advances (e.g., Einstein)
• achieved absurdly high levels of skill at sports (e.g., Jackie Joyner-Kersee, who set the long-standing world record in the seven-event Olympic sport “Heptathlon”)
• reached extraordinary heights in politics (e.g., Margaret Thatcher)
• realized incredible artistic achievement (e.g., Mozart)

Here is my proposed list:

Predictors of Extreme Success

PRACTICE
(1) Head start – starts training in relevant skills at a very young age
(2) Deliberate practice – many thousands of hours of deliberate practice (i.e., performing relevant activities at a level that is challenging while receiving rapid feedback on performance and mistakes)

MENTORSHIP
(3) Encouragement – has a person who recognized their talent or believes strongly in them, ideally from an early age
(4) Coach – works closely with a very talented coach or mentor

BELIEFS
(4) Growth mindset – believes they are capable of improving their most important personal characteristics through effort (rather than viewing them as fixed)
(5) Changeable world – belief that the world is changeable along the dimensions where they want to make change
(6) Confidence – a conviction in their own abilities and belief that they will ultimately succeed at their goals

GOALS
(7) Focus – maintains their major high-level goals for a very long time, rather than switching what they are aiming at
(8) Ambition – has extremely lofty goals

MOTIVATION
(9) Persistence – does not give up when very large obstacles stand in the way of their goals, even when failure looks imminent
(10) Effort – puts an enormous amount of time and emotional energy into achieving their goal
(11) Hunger – willing to sacrifice many things for success, such as comfort, fun, rest, being likable, etc.
(12) Passion – extreme passion or obsession in the domain related to their goals

ENVIRONMENT
(13) Opportunities – there exist high levels of opportunity both in the physical place where they are born or move early on (i.e., their region) and in the domain that they work in (i.e., their field)
(14) Privilege – is born with characteristics that are not heavily discriminated against in that time and place, or that are viewed in a positive light by the surrounding society

RESOURCES
(15) Resources – starts out with useful resources (e.g., connections, money, equipment, prestige)
(16) Luck – happen to be in the right place at the right time to seize certain opportunities (through no effort on their own part)

SKILLS
(17) Intelligence – high level of intelligence (e.g., as measured by IQ tests)
(18) Creativity – has unusually high skill at coming up with new, valuable ideas
(19) Decision-making – when very important decisions must be made, makes them in a carefully considered, thoughtful, rationally oriented, less biased manner that weighs many factors
(20): Resourcefulness – readily generates effective strategies for overcoming challenges that others may not have thought of or considered
(21): Prioritization – continually refocuses their efforts on the activities that yield the greatest output per unit input towards achieving their goals, and cuts out low leverage or distracting activities
(22): Self-control – not generally succumbing to short term gains at the expense of long term ones, and having conscious control over the choices they make rather than tending to be controlled by passing urges
(23): Aptitude – some people have an innate ability that makes them better at certain activities relevant for success in specific domains, for instance, a sprinter who is born with “fast-twitch” muscles, or an intellectual who was born with a mind that is much better than most at seeing connections between disparate things.

PERSONALITY
(24) Charisma – others are drawn to this person upon meeting them, or are easily influenced by what they say, for instance, believing this person’s claims, wanting to help this person, wanting to bet on this person’s success, wanting to be led by this person, etc.
(25) Conscientiousness – stays organized, pays attention to important details, is careful in their work, not sloppy
(26) Rule bending – circumvents rules, conventions, or social norms that stand in their way towards success, without breaking them egregiously in a way that will cause them to end up ostracized or in jail
(27) Courage – lacks fear in the face of difficult goal-related challenges that would make most people afraid, OR carries out important behaviors anyway as though lacking fear (even if experiencing extremely intense fear)
(28) Risk-taking – is willing to take large risks when the predicted average reward / expected value is large enough but doesn’t take excessive risks when the potential for reward is not great enough
(29) Demanding – requires those that help them or work with them to produce at a very high standard of excellence
(30) Perfectionistic – continually hones their work, not stopping until it achieves an extremely high standard of excellence

BEHAVIORS
(31) Promotion – actively markets the work that they do to make others aware of it and excited by it
(32) Responsiveness – seeks external feedback, criticism or reliable data about what they are doing, pays close attention to what is and isn’t working well, and readily adapts approaches based on this information when evidence shows things aren’t working
(33) Community – purposely surrounds themselves with people who have relevant skills, advice, encouragement, knowledge, influence, etc. and who is a positive influence in the direction of them achieving their goals
(34) Single-mindedness – disregard for other, non-goal related activities (which could include neglect of personal life or of non-goal-related responsibilities)

It’s an interesting exercise to consider: which of the many factors above do you think are MOST predictive of success?

How to Test a Theory Without Doing An Experiment:

Think of as many things as you can that are predicted by your theory, and try to identify at least one prediction that has the following four properties:

(1) your theory implies it is very likely to be true.

(2) it seems very unlikely to be true if your theory is false (i.e., there is no reason you can see to make that prediction other than if you believe in your theory). You may want to ask others if they agree that the prediction seems very unlikely to be true from the perspective of those who don’t believe your theory; otherwise, you might be deceiving yourself.

(3) you don’t already know the answer to whether it is actually true or not (you are merely guessing it is true because it is implied by your theory). This is important because if you already know the answer, you may be tricking yourself one way or another (e.g., by having already used this evidence in the construction of your theory, or by choosing this particular test because on some level you know it will make your theory look good).

(4) you can reliably look up whether that prediction is true or not. Then all you have to do is look up whether the prediction is true!

If it turns out to be true, that provides evidence to support your theory. More specifically, the more likely that true result is given your theory, and the less likely that true result is (to someone who doesn’t know your theory or if your theory is invalid), the more evidence the test provides. To explore the math for only a moment: that test of your theory should change your prior odds (i.e., your estimated probability that your theory is true, divided by the probability that it is not true) by using what’s known as the “Bayes factor,” namely, your estimation probability. Looking up the answer to that question would yield the result it did, if your theory is true divided by the probability it would yield that result if your theory is not true.

In other words:

odds your theory is true = ( probability of that result if your theory is true ÷ probability of that result if your theory is false) × prior odds your theory is true from before the test

Odds can be confusing to think about (remember that it is the probability that something is true divided by the probability that it is false). For instance, odds of 2 would mean that a thing is twice as likely to be the case then it is to not be the case.

It’s worth noting that the above procedure can work really effectively to test your hypotheses for yourself, but it’s NOT a good way to provide evidence that your theory is true to other people that don’t trust you (i.e., your honest pursuit of truth or your competence). The reason is that it’s easy to claim you conducted this fair test of your theory, when in fact, you already knew the answer to how the test would turn out (and therefore were incentivized to choose this particular test of your theory rather than another). Or perhaps you interpreted your theory in a weird way or tweaked it after the fact to make it look like it correctly predicted the outcome when it actually didn’t. Or maybe you screwed up one of the steps accidentally. In other words, the evidence this procedure produces is only trustworthy if you really trust the rigor and character of the person who produced the evidence.

This is why experiments are, at least in theory (but unfortunately too often not in practice), a better way to gather evidence when you need to communicate those results to others who don’t trust you, especially if you pre-register your study and analysis plan, and if you release your materials and the data, you collect.

If you’re interested in the concepts underpinning the approach I’ve described here, you may want to check out our program on Bayesian thinking (which you may or may not be happy to know uses almost no math):

http://programs.clearerthinking.org/question_of_evidence.ht…

The results of the predictive model surprised me! 

Before scrolling down, take a moment now to try to guess the top 5 variables that you think would be useful in predicting the severity of a person’s depression! I tried to include a very broad range of demographic and personality variables in the model – testing 91 in total.

Important note: these variables are predictive, not necessarily causal. In each case, we aren’t certain whether a higher incidence of that variable leads to worse depression, whether more severe depression causes a higher incidence of that variable, or if some outside factor happens to cause both depression and that variable. Also note that the variables shown below those that were most predictive of depression when controlling for all the other 90 variables. That means that a one standard deviation increase in each of these variables was associated with a greater increase in PHQ9 scores than any of the other variables (when training a model with all the variables at once). But all but of these variables are all associated with depression in the same direction on there own (when not controlling for other variables) except for one exception that is mentioned.

Variables That Most Strongly Predicted Depression

(1) Introversion (related to the Big 5 personality trait of Extroversion), as measured by the statements: “I see myself as extraverted, enthusiastic” or “I see myself as reserved, quiet”

(2) Feeling Poorly Rested after sleeping their ideal number of hours, as measured by the question: “Typically, how rested do you feel upon waking when you’ve just slept your ideal number of hours?”

(3) Under-sleeping, that is, the amount of time lower than their ideal hours of sleep that they sleep per night, calculated by subtracting actual average hours of sleep from reported ideal hours.

(4) Poor Treatment From Caregivers in childhood, as measured by the question: “Overall, how well were you treated by the people who raised you (while you were growing up)?”

(5) Spirituality, as measured by the question “How spiritual do you consider yourself to be?” Interestingly enough, the correlation of this variable with the PHQ9 depression score was VERY weak (r=0.02) in itself, it became strong only when controlling for the others. This is a weird exception, as all the other variables mentioned here have strong correlations with depression, whether measured on their own, or among controlled variables. 

(6) Low Levels of Conscientiousness, as measured by these two statements: “I see myself as disorganized, careless” or “I see myself as dependable, self-disciplined.”

NOTE: I did not include the Big 5 trait “emotional stability” a.k.a. “neuroticism” in the regression, since it contains aspects of depression already in its questions.

So this suggests that depression is linked to introversion, poor sleep quality, under-sleeping, poor treatment from caregivers growing up, spirituality, and low conscientiousness. Remember though that in each case it could be that it causes depression, is caused by depression, or is caused by something that also causes depression.

The predictive model I used was Lasso regression, with cross-validation, since I expected many of the variables to have almost no relationship to depression (i.e., the results would be sparse). The training set had 696 points, and the test set had 174 points. The training set error was: 54.7% of variance remaining, and the test set (i.e., out of sample error on new data) was 57.5% of variance remaining, so the results appear to be statistically robust (i.e., there appears to be very little overfitting).