Are you convinced that the shuffle mode on your iPod is messing with your mind? Or that certain numbers are bound to come up in the next lottery? If yes, you may be holding on to some serious misconceptions about randomness. Here’s what it means for something to truly happen by chance.

Image: CJM Grafx/Shutterstock.

Randomness is an often misunderstood concept. Unlike a lot of precise maths, randomness deals with ineffable concepts like odds and probabilities — concepts that our brains simply haven’t evolved to fully process and grasp; indeed, our brains are actually wired to find patterns and meaning in things that aren’t really there.

Another problem is that randomness tends to mean different things to different people. For some, randomness suggests a total lack of order in a sequence of symbols or steps such that there’s no intelligible combination or patterns. Others, namely scientists and mathematicians, describe it as a simple lack of predictability. Randomness in this context implies a certain measure of uncertainty.

But regardless of definition, people still make a lot of mistakes when thinking about it. Here are some of the most common misconceptions about randomness.

## Picking Out Patterns

I’m having a never-ending debate with my father that’s nowhere close to being resolved. When he sets his MP3 player to shuffle mode, he’s convinced that it plays songs in accordance to some kind of system. For example, he complains that it alternates genres on an eerily consistent basis, and that it favours particular songs and artists.

iPod image: VideoJug.

To be fair, I think many of us feel this way. Our brains can’t help but pick out patterns or tendencies in what are otherwise completely random events. And indeed, I often find myself anthropomorphising my iPod when it selects an apparent sequence of songs that go very well together.

What’s more, people tend to apply a different definition of randomness than what’s conventionally accepted. When we set our MP3 players to random, what we’re essentially asking the device to do is play the songs in the playlist in an evenly distributed manner. By playing songs by the same artist back-to-back, for example, our MP3 players violate this expectation.

But playing songs in a perfectly distributed manner is *not* random. In fact, it can been as something that’s highly structured. Hearing a song by the same artist back-to-back, or even back-to-back-to-back, *is* a random occurrence. It’s improbable, sure, but it’s random.

Relatedly, when listening to a playlist, it’s also important to keep a sense of *probability* in mind. For example, if a playlist contains 50 heavy metal songs, but only 25 dance tracks, the shuffled playback will be representative of this proportion; you’re simply going to hear more heavy metal songs given their predominance in the overall sample.

## Items Are ‘Due’ To Come Up

It’s also important to note that the preferences of your shuffle mode could be affecting the apparent randomness of your playback. Some MP3 players allow you to tweak the strength of the randomness (for example, favouring starred songs or certain artists), and some modes remove songs from the playlist as they go (that is, non-repeat). Both of these will have a profound impact on the “randomness” of the playback.

But if the playlist is set such that every song is thrown back into the pool, there’s absolutely no guarantee that an unplayed song is somehow “due” to come up before the ones that have played before it.

Cards: Vasilchenko Nikita/Shutterstock.

Another example is taking a Jack of Hearts out a 52 card deck, putting it back in, shuffling the deck, and pulling it out again. While surprising, it was just as likely as pulling out any other card. The deck — or your MP3 player — doesn’t “remember” what came before.

Similarly, some numbers or items are said to be “cursed” or “blessed” because they have been previously observed to either come up rarely or frequently. Again, in a truly random system, no such claim can be made.

## All Possibilities Are Equally Likely

Randomness happens, but it often happens within an overarching system. It’s often assumed, however, that randomness implies open-ended outcomes — that virtually *anything* can happen. Critics of Darwinian natural selection complain, for example, that there’s too much apparent design in evolution for randomness to be a guiding principle, and that there needs to be a divine overseer to help direct the process.

What the ID crowd fails to realise, however, is that evolution does function according to some very specific rules and constraints. Mutation, while giving the appearance of randomness, is indeed chaotic or stochastic in the sense that copying errors are constantly being made during sexual reproduction. But what is absolutely *not* random is the selection that follows; just because a mutation happens doesn’t mean it will be favoured by the environment. And in fact, most mutations are detrimental to organisms.

Computational cell biologist Kathryn Applegate looks at it from another perspective:

Sometimes, the word random is used to mean

unbiased. If you want to know who will win a political election, you make sure to poll a random sample of people, not just those hanging around a Tea Party rally. But the word random doesn’t have to mean that all possibilities are equally likely. When maternal and paternal chromosomes get together during conception, they exchange long sequences of DNA in a process called recombination. We now know that recombination happens more often in some places of the genome than others, but the specific sites where it will occur in a given embryo are impossible to predict. So recombination is random in the sense that it is unpredictable, but not in the sense that all outcomes are equally likely.

## Disorder Ensues From Randomness

Anyone who studies fractals knows this isn’t true. Random fractals use stochastic rules (that is, random, non-deterministic rules) to create all sorts of interesting patterns. Examples include self-avoiding walks and Brownian trees:

Also, Sierpinski triangles are created by rolling die and following simple rules. Fascinatingly, if 100 people independently rolled die 100 times and followed the same rules, each one of them would end up with something that looks like this:

## All Of Nature Is Potentially Predictable

There’s also a misconception that much of the randomness we observe in nature, like our failure to chronicle a truly Newtonian clockwork universe, is simply on account of our inability to properly measure all the variables at play. Eventually, the thinking goes, we’ll be able to make accurate predictions in previously — or seemingly — random systems.

But two 20th century discoveries have largely overturned this notion, namely quantum physics and chaos theory.

As Heisenberg’s principle of uncertainty has shown, we cannot be certain of a particle’s location *and* its momentum; we live in a universe of fuzzy probabilities. Similarly, the chaotic and dynamic inner workings of a hurricane will forever be impossible to predict with perfect precision.

So between the two, we are fundamentally — and irrevocably — limited in our predictive power.

There may be hidden aspects that influence random. Take IOS as an example. It generates a random list of songs based on your play criteria eg. A genre like heavy metal or country music. If you select a different song, it will keep the list and you may hear the same sequence of songs again. To reshuffle and generate a new random list, you need to deselect and reselect shuffle again.

So, sometimes understanding the underlying implementation of a system will help recognise why some things aren’t random.

There are patterns that you can also see that influence randomness. People are more likely to select a smooth stone over a rough one (even if they can’t see it), or LifeHacker will almost always post an article about CVs with a woman in the picture. This is effectively pseudo random (as there is a definite, predictable trend).

You also needed to mention that random number generators in computers (e.g. an iPod) aren’t truly random and that they are pseudo-random.

A good example to show that most people don’t understand probabilities is the Monty Hall Problem.

Go ahead, define ‘truly random’.

If something is physically and mathematically impossible to predict the results, then the results are truly, fundamentally random.

Things like quantum phenomena (mentioned above in the article) and digits of pi are truly random.

The digits of pi are utterly deterministic and predictable.

Calling them random seems very strange.

I’m thinking of a specific animal right now, I’ll tell you which animal if you like.

It’s physically and mathematically impossible for you to predict which animal.

By your definition, it’s random.

Did you really intend to define ‘random’ as simply something you have insufficient data to predict?

Also consider cognitive bias. If you are convinced your MP3 player isn’t playing tracks in a truly random order and start looking for a pattern in what it

isplaying, you will probably find one.I hope that I see a lottery draw of 1 2 3 4 5 6 and 7 in my lifetime. Thouands of maths teachers will rejoice in their shared pittance.

I think there was a draw once where the numbers lined up in a single column. And because it was a common pattern that a lot of people used, the prize was split up many ways and no one got a big payday.

Absolutely, to play the lottery one should understand both math and sociology.

That said, if you understand math you’ll play few lotteries. ðŸ˜‰

Similar thing just happened in the UK. The Lotto numbers were multiples of 7: 7, 14, 21, 35 and 42 and more than 4000 people picked that combination so they each only ended up with 15 quid.

http://www.bbc.com/news/magazine-35893628