Perfect Major?

The intertubes are full of quizzes. Magazines like Cosmo have thrived on them for years. “Are you a good lover?” Websites like Tickle pretty much consist of nothing else (and I haven’t bothered beyond the odd quiz someone sends me). Tons of Facebook apps like Flixster (movies) and Harry Potter rely on them heavily. One of my google alerts is for linguistics and I saw some random 14-year-old dude’s blog post about his perfect major according to this quiz. My results are below the jump.

So of course everyone with sense knows these quizzes are pretty much random. However, they also collect a vast amount of data. What they don’t collect (usually) is actual information about the people who take their quizzes. Imagine if at the end of a quiz there was a question or two about the actual truth of the thing the quiz is predicting. What kind of lover are you? Well just ask! If the result is similar to the quiz results, you can gauge how well your quiz is classifying people. It may not produce scientifically valid results but it does produce results that are better than nothing.

So I took the quiz to find my perfect major. The majors it has are fairly general. Also, I’m going to go out on a limb and assume that the person who developed the quiz relied on nothing more than his intuitions when creating the scoring function. The person who created the quiz also created these lovelies:

• Which celebrity personality is your best match?
• What is your Wedding day theme song?
• What should you be named?
• What is your breakfast spread?

So — probably not an expert on career planning. However, given the quiz, if we could collect voluntary data from the users about what their major (or planned major) actually is and whether they felt they were pursuing the major they actually want, we could tune this quiz to give better results.

This is a meme that I have caught lately. Take a rule-based approach and add statistics to it. For example, in chess a common opening move is King’s pawn to e4. We could build a chess program that just makes that move whenever it’s white. Or we could look at all the possible moves it could make and a collection of all the moves made by grandmasters in the past 40 years (who went on to win the game). If pawn to e4 has occurred 34% of the time in winning games, make that move 34% of the time. Playing an entire game of chess like this would be impossible, but it’s a relatively painless way of bootstrapping your chess program with good opening moves. That is, assuming you can collect data from grandmaster games.

My results: