`ttestBF`

outputIn the Evidence worksheet, we did the following Bayesian t-test:

`ttestBF(formula = income ~ sex, data = data.frame(cpsdata))`

```
Bayes factor analysis
--------------
[1] Alt., r=0.707 : 18.25138 ±0%
Against denominator:
Null, mu1-mu2 = 0
---
Bayes factor type: BFindepSample, JZS
```

Here’s a more detailed explanation of the output of that test – we’ll go through each bit in turn:

`Bayes factor analysis`

- You’re doing a Bayes Factor analysis.

`[1] Alt., r=0.707`

- In order to calculate a Bayes Factor, R has to make some assumptions about how big the difference is likely to be. The `ttestBF`

command makes some broad assumptions that cover the range of effect sizes typically seen in psychology.

More specifically, `ttestBF`

assumes a Cauchy distribution of effect sizes centered on zero, with a scale of 0.707 – this is where `r=0.707`

comes from. That description probably didn’t make much sense useless you have a very strong maths background, so here’s the same idea, shown as a density plot. It’s basically an assumption that small effect sizes are quite likely, and large effect sizes are quite unlikely.

`18.25138`

- The Bayes Factor (i.e. the main result of this analysis). See the next section for further discussion

`±0%`

- Basically a confidence interval on the Bayes Factor. It’s 0% here because we have so much data, but with smaller samples we might see something like `20 ±5%`

, which would means the Bayes Factor is about 20, give or take 5%. So, it’s between 19 and 21.

`Against denominator:`

- This tells you that the null hypothesis is the denominator of the fraction that is used to calculate the Bayes Factor. So, in other words, you’re getting BF_{10} rather than BF_{01} – see the Evidence worksheet.

`Null, mu1-mu2 = 0`

- This tells you that the null hypothesis is that the difference between the two group means (called `mu1`

and `mu2`

here) is zero.

`Bayes factor type: BFindepSample`

- You’re doing a Bayes Factor analysis; `indepSample`

is short for `independent samples`

, which is another way of saying “between-subjects test”. `JZS`

is short for Jeffreys, Zellner, and Siow - the surnames of three people who are credited with coming up with this particular way of working out Bayes Factors.

The Bayes Factor reported by the above analysis is sometimes described as the relative likelihood of a difference, compared to the absence of a difference. Under that description, the Bayes Factor reported above means it’s about 18x more likely there is a difference than there isn’t. But that’s a bit of a simplification.

This way of describing a Bayes Factor is only accurate if, before collecting the data, you thought the presence or absence of a difference were equally likely outcomes. If that is not the case, it is more accurate to consider the Bayes Factor as the extent to which you should update your beliefs. For example, say you think telepathy is really unlikely, perhaps because there is no known mechanism by which it can operate. You might express your skepticism by saying it’s about million times more likely telepathy doesn’t exist than it does (a ‘1 in a million’ chance). Say you then run an experiment on telepathy and get a Bayes Factor of 20. This tells you you should adjust your belief from 1 in a million to 1 in 50,000. So, after the experiment, you still think it’s more likely telepathy doesn’t exist than it does. Bayes Factors are a way of rationally updating your beliefs as new data becomes available.

What if your p-value is less than .05, but your Bayes Factor is less than 3? The short answer is … believe the Bayes Factor, and ignore the p value. The p value is there for historical reasons and doesn’t tell you anything you actually want to know.

Recall that the p value doesn’t tell you anything that is both easy to understand and useful to know. For example, it *doesn’t* tell you the likelihood of the null hypothesis, given your data.

Recall also that BF does give you something you want to know — how much more likely your experimental hypothesis is than the null hypothesis (e.g. BF = 3 means experimental hypothesis is 3x more likely)

Now, take a look at Wetzels et al. (2011). In situations where .01 < p < .05, most of the time Bayesian analysis indicates the evidence is anecdotal (i.e. BF < 3). If you were going to stick to p values, you wouldn’t pick the p <.05 threshold – it’s just far too lenient.

Then, take a look at Masicampo & Lalande (2012) … p values in Psychology are very commonly in the .01 < p <.05 region.

Put that all together and we begin to get a sense of one of the causes of the replication crisis, and how BF may help us escape it.

In our discussion of the Bargh paper in the Evidence worksheet, we estimated the probability of the null prior to data collection as .95. We also said that the probability of the null, after a t-test with p = .049, was around 50:50. This calculation also makes use of Bayesian maths, you can find details of the calculation here.

`anovaBF`

outputIn the within-subject differences worksheet, we ran a single-factor within-subjects Bayesian ANOVA that produced output something like this:

```
Bayes factor analysis
--------------
[1] congru + subj : 43834950 ±2.29%
Against denominator:
rt ~ subj
---
Bayes factor type: BFlinearModel, JZS
```

The first part of this output is similar to the output for `ttestBF`

. However, we then come to:

```
Against denominator:
rt ~ subj
```

which is a bit different to `ttestBF`

. Recall that a Bayes Factor is a fraction - it’s the evidence for one hypothesis divided by the evidence for another. The top of that fraction (the *numerator*) is given in the first part of the output as `congru + subj`

. The bottom of that fraction (the *denominator*) is given as `rt ~ subj`

.

The denominator is the *null* hypothesis that reaction time is only affected by participant ID. In other words, it’s a hypothesis that different people have different reaction times. This is compared to the *experimental* hypothesis (the *numerator*), which was given earlier as `congru + subj`

. This is the hypothesis that trial type (`congru`

) has an effect on reaction times **and** that different people have different reaction times (`+ subj`

)

When we take the evidence for the experimental hypothesis (`congru + subj`

) and divide it by the evidence for the null hypothesis (`subj`

), we get a Bayes Factor that reflects the evidence for there being an effect of trial type.

There’s one further part of the output:

```
---
Bayes factor type: BFlinearModel, JZS
```

This is says two things. First, it tells you which type of Bayesian calculation you’re doing - `BFlinearModel`

. ANOVA, and a number of other techniques, like regression, are special cases of a general approach called “General Linear Models”. So, in pragmatic terms, this is just a not-very-clear way of saying you’re doing an ANOVA. Finally, `JZS`

has the same meaning as previously - it’s short for Jeffreys, Zellner, and Siow - the surnames of three people who are credited with coming up with this particular way of working out Bayes Factors.

In the factorial differences worksheet, we also calculated a Bayes Factor for an interaction. It produces output like this:

```
Bayes factor analysis
--------------
[1] medit + congru + medit:congru + subj : 5963.887 ±3.28%
Against denominator:
rt ~ medit + congru + subj
---
Bayes factor type: BFlinearModel, JZS
```

The main difference here, relative to the last output we looked at, is that the denominator is different. As explained in the main worksheet, we calculate the Bayes Factor for an interaction by dividing the Bayes Factor for a hypothesis including the interaction and main effects, `medit + congru + medit:congru + subj`

by a hypothesis including everything just the main effects, `medit + congru + subj`

. In the output, this is shown by given `medit + congru + subj`

as the denominator.

This material is distributed under a Creative Commons licence. CC-BY-SA 4.0.