Strategy. Innovation. Brand.

I understand inferential statistics reasonably well but I’m a rookie at Bayesian statistics. What’s the difference? Well, I’m glad you asked.

Inferential statistics allow us to infer some conclusion. Let’s say we want to study a hypothesis that *Scientific American* put forth last year: Men who do more housework have less sex. That’s an interesting thought and one that men – especially married men – might want to investigate.

Where do we start? First, we need to define the population. Let’s say we’re only interested in men in America. So, the hypothesis becomes:

Men in America who do more housework have less sex.

Now, all we have to do is interview all the men in America about the chores they do and how often they have sex. There are roughly 147 million men in America so this is going to take some time. Indeed, it will take so long that our attitudes toward sex and housework might change in the interim. The study would be useless.

We need a faster way. We might take a random sample of men and interview them. We gather the data and find that the hypothesis is true *in the sample*. The men we interviewed who did more chores also had less sex.

Now we have to ask ourselves, what’s the probability that the sample accurately represents the population? We found an inverse correlation (more housework, less sex) in the sample. Can we infer that this is also true in the population?

To do this, we need to play with probabilities. We randomly selected the men in the sample, which means the sample *probably* represents the population (but maybe not). Generally speaking, larger random samples are more likely to accurately represent the population.

We can also calculate the probability that the correlation found in the sample also exists in the population. By general agreement, if it’s more than 95% probable (less than 5% improbable), we declare that the finding is *statistically significant*. In other words, we believe that the finding is real and not caused by errors in the way we chose the sample. We infer that it exists in the population as well as the sample.

Two things to note here:

- Statistical significance has to do with probability, not size. It’s not the same as saying, “Tom is significantly smarter than Joe”. A statistically significant difference may be quite small.

- Five per cent of the time – one time out of twenty – the finding is flat out wrong. Yikes! The five per cent threshold is generally used only in the social sciences. In the medical sciences, we normally use a one per cent or one-tenth of one per cent threshold to declare statistical significance.

Note that our finding – which is called a frequentist probability — represents a point in time. It’s essentially a snap shot. We noted that, if our study takes too long, conditions might change and invalidate the study. Indeed the *Scientific American* study cites data collected from 1992 to 1994. Perhaps conditions have changed since then. So how accurate is this?

That’s where Bayesian statistics come in to play. They allow us to add in new information as it becomes available. Let’s say, for instance, that women’s attitudes toward men who do house work evolve over time. We could factor in the new information and recalculate the probabilities.

Bayesian statistics are complicated and hard to compute. We’ve only been using them widely in the recent past as more powerful computers have become available. Still, they can help us work out complex problems that used to be way beyond our capabilities.

I’ll write more about Bayesian probabilities as I learn more. In the meantime, I’m going to sell the vacuum cleaner.

Another factor to consider is that people regularly lie about their sex lives. Maybe the men lied (said they got less sex) to get out of housework.

The popular stereotype is that men lie the other way … claiming to get more sex than they actually do.