Power BI now has a new feature called error bars that show "Uncertainty" in the data on the graph to users. Uncertainty is the quantitative estimation of error present in data, it is an important aspect to data, especially in scientific disciplines and other fields to give an idea of how precise the measured data is. Error bars add context to a chart and can give users an idea of how precise a measurement is and how far from the reported value the true value might be.
To use the error bars feature, first select the visual to be analyzed, then select the Analytics subpanel in the Visualization panel, and finally Error bars. But if you are using an older version of Power Bi desktop, enable the error bars feature in Options & Settings > Options > Preview Features and check the error bars checkbox.
In Power BI, the error bars can be visualized with:
By field: You can use the value of some other field for the upper and lower bound of each error bar.
By percentage: Will show you upper and lower bounds as calculated from the displayed value of your value field.
By percentile: These will show you bounds calculated from the aggregated data points at each X-axis value on your chart.
By standard deviation: The usual statistical treatment is to show error bars within two standard deviations of the mean.
For example, a company have data of average sales in one year, they want to measure risk assessment using the data that they have, to measure it, use standard deviation in error bars, the standard deviation can be used as a tool to measure the volatility of a fund. Higher the standard deviation, the higher the volatility. This measurement can help assess risk when deciding the next solution to maintain or improve the sales to avoid the risk.
To use error bars by standard deviation, select your visual that you want to analyze, in Visualization panel, select Analytics sub panel, enable the error bars then change the type into standard deviation, then choose standard deviation that you want (1, 2, 3, etc.).
Error bars by standard deviation
Another example is a palm oil company that has data on daily palm oil production. They want to know whenever there is a time when the production is outside the ideal values to make sure there is longevity and production goes smoothly by hitting financial targets. To visualize it, use error bars and choose type "by field," then input the upper and lower bands of the ideal values, with that, we can see the value that is outside the ideal in the graph.
Error bars by field
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