First, how can graphics and/or statistics be used to misrepresent data? Where have you seen this done?

Second, image that you are a human resources (HR) analyst reporting to a recently hired supervisor. Your supervisor has asked you to provide an example of data processed by HR.

Describe one type of data that an HR department may process, and discuss a corresponding statistical method that may be used to summarize the data.

Experts in the field of statistics concur that we can never be completely certain that our findings are accurate. Due to this, we instead construct 99% or 95% confidence intervals rather than 100% confidence intervals. This is due to the fact that our results and graphical representations can occasionally mislead the data.

Graphics have the potential to deceive.

If graphs accidentally give the wrong impression, we will declare them deceptive.

Graphs are deemed misleading if they intentionally give the wrong impression.

The scale of the graph, an inconsistent scale, or an incorrect origin are the most typical graphical errors in data representation.

Scales for comparative graphs should be the same, and increments between tick marks should be constant. A graph that starts at a higher or lower number can be deceptive since readers typically think that the baseline, or 0 point, is at the bottom of the graph.

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