Bar plots are nonsense

I was once again utterly stupefied by the amount of bar plots I had to endure during the CC-PM retreat in the beautiful Kartause Ittingen last weekend. Like this one...

from Beaton et al., 2015, Mol Met, dx.doi.org/10.1016/j.molmet.2015.08.003

OK, to be honest that one is not from last weekend - but there were many like it. If you don't believe me, take a stop watch and check how long it takes you on Google Scholar to find one of these useless bar plot - usually less than a minute inside the life sciences.

What is bad about these plots, you ask? Well, put simply, they couldn't be more misleading. There are several issues with this nonsensical way to represent different samples of measurements, like for instance the amount of 14C-Clucose per well.

1. Spread/variation of data versus precision of estimation

The general goal of your average PhD student at a scientific conference, retreat or whatever these events might be called nowadays, is to show that a group of measurements she has done on a control is less (or more) than a group of measurements she has done on a sample, which was in some way disturbed from being a control - usually coined treatment.
Finally, a difference between two groups of measurements is qualified using a statistical test, for instance a t-test, if your data is really nice, or a Wilcoxon rank-sum test, if your data is kind of naughty. However, it is - at least from a marketing perspective - useful to find a way to visualize your results in some way.
Now, there are two things you might want to show when illustrating a group of observations:

1. The spread/variability of the group.
2. How good you were in estimating some kind of summary of a group, i.e., the mean value.

While the first case might seem intuitive, the latter might not. However, often we replace groups of observations with a summary measure. If we do, then again we often use the mean value - or in coloquial terms average. However, computing the mean value of a sample is generally understood to be an estimation of the population mean. As most of my readers will know, the more data we have (or the larger your sample is) the better we can estimate the population mean. The precision of which is most appropriately indicated by using a confidence interval around the sample mean. Note, that it is nor the standard error, but the confidence interval, which in its approximative form spans twice a standard error in each direction! Nevertheless, most barplots yield standard error bars...

Anyhow, adding either a confidence interval or a standard error to a mean value has no descriptive power for the distribution of the data - or variability, or spread!

2. Bar plots cannot show you differences 

 Let's look at the following example:

On the left we have a selection of six groups each with 20 observations. Clearly, these groups are not the same when we look at the scatter plot. However, when using a bar plot it seems that everything is the same in these groups. Even the standard error bars indicate no difference. Probably, we messed up the experiment or something.
If instead, we use the much more useful box plot, we immediately identify different groups. Even more forensic is the use of violin plots, which show the mirrored probability density of the data and as such allow for the identification of bi- or multimodal distribution of the data.

Try it yourself on https://stekhoven.shinyapps.io/barplotNonsense

3. There might be a bright future

I actually have to be honest to you once more, the first chart I found, wanting to show the distribution of multiple groups of measurements was this one:

from Sonay et al., 2015, Genome Res, doi/10.1101/gr.190868.115

Not only, this is a great way to indicate the difference between multiple groups - using a combination of violin and boxplots (well, you have to pay attention!) - but also the author uses Hadley's ggplot2 ... so maybe the is still a bright future ahead! I am convinced of it!