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

ADDITIONAL DETAILS 

How can graphics and/or statistics be used to misrepresent data?

Introduction

You’ve just been handed some data, and you want to share it with your boss or colleagues. You open up the spreadsheet and start scrolling through the rows of numbers with a sense of excitement and anticipation. At first glance, everything looks fine: there are no red flags in this dataset—but then something catches your eye… Aha! This graph looks interesting! Maybe you can use this information to show how things are changing over time? Or maybe we need more detailed statistics here…

Images can be cropped to show only part of the data or story.

Cropping images is a great way to distort the data or story. For example, if you’re trying to convince someone that they should buy a particular product, crop out the part of their body where they have acne or stretch marks and show only how beautiful they are. This might work on some people, but it’s likely to make others feel insecure about themselves and their appearance—which could cause them not buy your product at all!

Another way cropping can be used for misrepresentation is by highlighting certain facts in an image while leaving other facts obscured (or even hidden). This makes it seem like there are more positives than negatives in an issue when really there aren’t any significant differences between groups being studied—and thus making your point easier than it actually is.

Graphics can be distorted to make a point.

In general, graphics can be used to misrepresent data. This is true for any type of graph or chart, including bar graphs and pie charts.

For example, consider the following image:

This image depicts a fictional company’s sales for different types of products over time. It’s easy enough to see that the blue line representing “sales” has increased at an increasing rate since 2011—but what does this mean? Is it good news? Is it bad news? How do you interpret these numbers without context?

Graphs can be misleading without proper context, such as scale, sample size or timeframe.

Graphs can be misleading without proper context, such as scale, sample size or timeframe. For example:

• A graph showing the number of people who are uninsured in America is not helpful if you don’t know that the chart is based on data from 2013-2014. The trend may be changing now—in fact it has been changing steadily since 2010-2011—but it still shows a significant increase in uninsurance during this time frame.

• Another example would be a bar graph showing how many new cars were sold in each state over 10 years. It might appear as though there’s less demand for new cars today than there was 10 years ago (which could lead people to think that sales should decline), but if you look at longer periods of time (say 25 years), we see more growth overall!

Statistics can be cherry-picked or misused in other ways to mislead.

Statistics can be used to support any argument. This is because statistics are often used to show the overall results of a study, but they don’t necessarily show the whole picture. For example, if you’re trying to explain why your company’s profits have been declining for three years in a row, it might seem reasonable for managers and executives at your company headquarters (and perhaps even investors) that these results are due entirely to bad luck and bad decisions made by employees who aren’t responsible enough for their own actions—or maybe just lazy ones! But if we look at those numbers again with some perspective:

• We see that revenue dropped by 6% over each of those three years despite an increase in sales volume during this time frame;

• Our costs were also flat over that period;

• Our net income rose every year except one when there was a decline caused by bad weather conditions affecting travel demand – but even then it was still positive because our other sources of revenue offset part of its impact on profitability;

Graphs, charts and statistics are powerful tools for analyzing data, but they aren’t necessarily clear cut.

Graphs, charts and statistics are powerful tools for analyzing data. They can help you see patterns and trends in your data, which is especially useful if you’re trying to understand how things change over time.

But graphs aren’t always clear cut. There are many ways that a graph can be misleading or misrepresent the data it represents. For example:

• Cherry-picking graphics: You may have heard of this term before—it refers to the practice of selecting only select parts of an image that support one side of an argument (e.g., using only one part of a graph when there were multiple variables involved) or selecting only certain numbers from a table when there were others that would have given similar results (e..g., switching all positive observations with negative ones).

• Cropping images: This involves removing some portion of an image so it appears differently than originally intended; for instance if someone takes out part of their photo before uploading it online then people will see something different than what they originally saw when they took the picture in real life!

Conclusion

The most important thing to remember when it comes to using graphics in your data analysis is that they can be misused. You need to make sure that whatever you’re using has been vetted by a reliable source, and that you have the right context for it. If something looks odd, ask yourself why – and if it might not be as straightforward as it seems on first glance.