# The p-direction: A Bayesian equivalent of the p-value?

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The Bayesian framework is powerful and allows for an incredible amount of flexibility and control over your analysis. That being said, newcomers often struggle with a lot of new concepts and tools and could benefit from some **familiar grounding**. And the *p*-value is a very familiar index (although paradoxically often misunderstood, but that’s another topic).

**Is there an equivalent of the p-value?** Well, depends on what “equivalent” means. Some might argue that the

**Bayes factor**is some sort of equivalent, i.e., a value that can be used for decisions and interpretation of results. Some others would suggest that the MAP-based

*p*-value is another alternative.

Based on a simulation study (Makowski et al., 2019), we think that the **probability of direction** (*p*-direction, or *pd*) is the closest ** statistical equivalent to the p-value**. The

*statistical*is important here, simply meaning that the two indices are strongly correlated. That being said, they are

**not**(as we argue in the paper, the

*conceptually*equivalent*pd*is an index of effect

*existence*, rather than

*significance*).

Here’s a short example.

## Frequentist regression

First, you can install (or update) the necessary packages by running the following (it’s important that the *insight* package version must be >= 0.8.1):

install.packages(c("insight", "bayestestR", "parameters"))

Let’s start by running a simple linear regression and displaying its result with the **parameters** package.

library(parameters) model <- lm(disp ~ carb, data = mtcars) parameters(model) ## Parameter | Coefficient | SE | 95% CI | t | df | p ## ----------------------------------------------------------------------- ## (Intercept) | 145.48 | 41.58 | [60.56, 230.40] | 3.50 | 30 | 0.001 ## carb | 30.31 | 12.87 | [ 4.02, 56.59] | 2.35 | 30 | 0.025

The *p*-value of the linear relationship between the two variable is of *.025* (the second row in the *p* column). What does a Bayesian analysis tells us?

## Bayesian regression (with flat priors)

As you might know, a Bayesian analysis is close to a maximum likelihood analysis (the typical frequentist paradigm) when no information is given by the prior (and the result is only driven by the data). This is the case with **flat priors**, that give equivalent likelihood to each and every one of your wildest dreams (see the *How to Specify Flat Priors (and why you typically shouldn’t)* section).

Let’s fit the same regression as above within a Bayesian framework with a flat prior (i.e., by setting them as `NULL`

).

library(bayestestR) library(rstanarm) model <- stan_glm( disp ~ carb, data = mtcars, priors = NULL, prior_intercept = NULL ) parameters(model) ## Parameter | Median | 89% CI | pd | % in ROPE | Rhat | ESS | Prior ## -------------------------------------------------------------------------------------------------- ## (Intercept) | 145.97 | [78.68, 212.98] | 99.92% | 0.11% | 1.000 | 55624 | Uniform ( +- ) ## carb | 30.24 | [ 9.47, 51.26] | 98.80% | 8.50% | 1.000 | 52688 | Normal (0 +- 191.83)

It tells us that the *p*-direction is of 98.80%, i.e., `0.9880`

(note that your results can slightly vary due to the random nature of the sampling; you can increase the number of iterations to get more stable results). We can quickly visualize its meaning as follows (with the **see** package):

library(see) plot(p_direction(model))

## From *p*-direction to *p*-value

We can convert this value to a *p*-value using the following function:

pd_to_p(0.9880) ## [1] 0.024

As we can see, we are not far from the frequentist *p*-value!

But again, we need to underline that the *p*-direction has a **different meaning and interpretation**. It refers to the *probability that the effect is positive or negative (depending on the median’s sign)*. But like the *p*-value, it cannot either be used to **support a lack of an effect** (for that, ROPE-based indices or Bayes factors might be more appropriate).

Moreover, when using **informative priors** centred at 0, a Bayesian analysis will always lead to “less significant” effects, as the prior will pull the posterior towards 0. This is a natural way of penalizing results, which **is a good thing**.

In conclusion, make sure you understand the indices you use (for instance by **checking-out our gentle intro to Bayesian analysis**)!

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