1.2.2 Plotting variability in data

Data obtained from a sample should be representative of the population of interest. Since it is rarely possible to include the entire population in a sample, there is always some degree of uncertainty or variability in how well the data represent the population (see the Sampling courses and Processing and analysing AMR data course for more information). It is good practice to indicate uncertainty in data either in text, tables or graphs. For graphs, uncertainty in the data is represented by error bars.

Error bars can be used to display the standard deviation, standard error, confidence intervals (e.g. 95% interval), or minimum and maximum values in a dataset. (To revise these terms, revisit the course Processing and analysing AMR data.) When using error bars on graphs, always state the measure used to represent uncertainty in the data as the measures are not the same.

Error bars are markers drawn over data points on a graph and either extend from the centre of a data point or the top edge of a column (such as a bar chart). You can get a sense of how precise the measurements are (which reflects the level of uncertainty in the data collected) by looking at the error bar's length: short error bars indicate less variability in the data, and in contrast, long error bars indicate more variability, that is, the values are more spread out.

You can also use error bars to gauge if there might be a significant difference between groups. Overlapping error bars may suggest there is not a significance difference between groups, while no overlap between error bars may suggest there may be a significance difference. You cannot conclude statistical significance by looking at the graphs alone, so always perform a statistical test when drawing conclusions. (For a recap on methods of testing statistical significance, revisit the course Processing and analysing AMR data.)

You often see error bars plotted on line graphs and bar charts, such as the one in Figure 2.

Activity 4: Plotting variability in data

Timing: Allow about 10 minutes

Maximise

Figure 2 Deaths attributable to bacterial antimicrobial resistance by syndrome in 2021 in South Asia (Bangladesh, Bhutan, India, Nepal and Pakistan) in all groups and sexes (IHME, 2025a)

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Bar chart of the number of deaths attributed to bacterial antimicrobial resistance by syndrome in 2021 in South Asia (Bangladesh, Bhutan, India, Nepal and Pakistan) in all groups and sexes. Error bars show the uncertainty range (IHME, 2025a).

Figure 2 Deaths attributable to bacterial antimicrobial resistance by syndrome in 2021 in South Asia (Bangladesh, Bhutan, India, Nepal ...

Figure 2 shows uncertainty intervals for the number of deaths attributed to antibiotic resistance for each syndrome. What can you say about the uncertainty intervals?

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Answer

The uncertainty intervals appear to be wider in some syndromes than others. For example, tuberculosis appear to have wider uncertainty intervals than peritoneal and abdominal syndromes. This may indicate that the data collected on tuberculosis was more variable than in peritoneal and abdominal syndromes.