How do you choose the best error bars for your data?

1 Types of error bars

There are many types of error bars, but the most common ones are standard deviation (SD), standard error of the mean (SEM), and confidence intervals (CI). SD measures the spread of individual data points around the mean, SEM measures the precision of the mean estimate, and CI measures the range of values that are likely to contain the true mean. Depending on your research question and the nature of your data, you may want to use one or more of these error bars to show different aspects of variability or uncertainty.

2 Pitfalls to avoid

One of the most common mistakes when using error bars is to confuse them with significance tests. Error bars do not tell you whether the difference between two means is statistically significant or not, they only show the uncertainty of the mean estimates. To test for significance, you need to perform a proper statistical test, such as a t-test or an ANOVA, and report the p-value or the effect size. Another common mistake is to use the wrong type of error bars for your data. For example, if your data are not normally distributed, you should not use SD or SEM, as they assume a normal distribution. Instead, you should use non-parametric methods, such as median and interquartile range, or bootstrap methods, to calculate the error bars.

3 Best practices to follow

To choose the best error bars for your data, you should consider the following factors: the purpose of your plot, the audience of your plot, the type and distribution of your data, and the conventions of your field. The purpose of your plot determines what you want to emphasize or compare in your data. For example, if you want to show the variation of individual data points, you may use SD, but if you want to show the precision of the mean estimates, you may use SEM or CI. The audience of your plot determines how familiar they are with the error bars and how much detail they need. For example, if you are presenting to a general audience, you may want to use simple and clear error bars, such as CI, but if you are presenting to a technical audience, you may want to use more complex and informative error bars, such as bootstrap or Bayesian intervals. The type and distribution of your data determine what error bars are appropriate and valid. For example, if your data are categorical or ordinal, you may use error bars based on proportions or ranks, but if your data are continuous or interval, you may use error bars based on means or medians. The conventions of your field determine what error bars are commonly used and accepted. For example, if you are working in biology or psychology, you may use SEM or CI, but if you are working in physics or engineering, you may use SD or error propagation.

4 How to create error bars

Creating error bars for your data can be made easier with the use of software tools and packages such as Excel, R, Python, MATLAB, and GraphPad Prism. Depending on the tool you use, you may need to specify the type of error bars, the level of confidence, and the direction and style of the error bars. You may also need to calculate the error bars manually or use a built-in function or library. To create error bars, you should first prepare your data and calculate the mean and the error bar values for each group or condition. Next, select a suitable plot type such as a bar chart, a line chart, a scatter plot, or a box plot. Then add the error bars to the plot using the tool's options or commands, customizing their appearance and format according to color, width, cap size, and transparency. Finally label the axes and legends and add a title and caption to explain the error bars.

5 How to interpret error bars

Once you have created your error bars, you should be able to interpret them and draw meaningful conclusions from your data. To interpret error bars, you should compare the error bars within and between groups or conditions to see how much overlap or difference there is. Additionally, use the rule of thumb that if two error bars do not overlap, there is a significant difference between the means at the 5% level. You should also use the level of confidence to estimate how likely it is that the true mean lies within the error bar range. Lastly, use the appropriate statistical test to confirm or reject the significance or effect of the difference between the means, and report the p-value or effect size along with the error bars.