For any scientific measurement, accurate accounting for errors is nearly as important, if not more important, than accurate reporting of the number itself.
For example, imagine that I am using some astrophysical observations to estimate the Hubble Constant, the local measurement of the expansion rate of the Universe.
I know that the current literature suggests a value of around 71 (km/s)/Mpc, and I measure a value of 74 (km/s)/Mpc with my method. Are the values consistent? The only correct answer, given this information, is this: there is no way to know.
Suppose I augment this information with reported uncertainties: the current literature suggests a value of around 71
2.5 (km/s)/Mpc, and my method has measured a value of 74
5 (km/s)/Mpc. Now are the values consistent? That is a question that can be quantitatively answered.
In visualization of data and results, showing these errors effectively can make a plot convey much more complete information.
A basic errorbar can be created with a single Matplotlib function call: