The Danger of Data Misinterpretation

Just because data is more accessible to broader audiences does not mean that its recipients are sufficiently equipped to interpret what they receive. People with little topical knowledge, statistical skills, and contextual awareness may draw inferences, be fearful of, or otherwise misinterpret data. Because we are constantly showered with numbers in the media, misinterpretation is a common problem when analyzing statistical data. Many of the statistics reported in the news, such as unemployment and divorce rates, might be deceiving. As a result, it’s fascinating to consider how newspapers from various regions might present the same facts in different ways.

Causes of Data Misinterpretation

More often than you might think, data is misinterpreted. Key aspects may be overlooked, or a situation may be oversimplified or overcomplicated, even with the best of intentions. Organizations may respond to trends that aren’t what they appear to be. Even when two persons look at the same analytical result, they may have different interpretations. Data can be misinterpreted in different ways and for a variety of reasons. Here are examples;

Insufficient Domain Expertise: Both domain expertise and data expertise are required for accurate data interpretation. On the other hand, business professionals are not data scientists, and data scientists do not typically have the same level of subject matter experience as other members of the firm. Although roles such as business analysts exist in the middle, an imbalance of data expertise and domain expertise can lead to data misinterpretation.

Important Variables Are Omitted: Data can be misinterpreted due to a single missing variable. When data is misinterpreted, it leads to wrong conclusions and, in some instances, poor investment decisions.

Aggregation Obscures Truth: A story can be told at several levels of aggregation. Knowing this, it’s typical to experiment with different levels of aggregation to validate the trend and figure out where the data diverge or reverse.

Inferences Are Off Base: Because all data interferences are conditional, it’s important to know which group the inferences are drawn upon. You risk inferring the inaccurate properties about a population if you don’t.

Sources of Variation Overlooked: To analyze data in a way that leads to insights, it’s critical to understand the sources of variation in a process.

Numerical Analysis Missed Something: Data visualizations can highlight inconsistencies that might otherwise go undetected in a numerical study. They could also demonstrate that what appears to be numerically interesting is an error. Outliers (extreme values that skew analysis), for example, are instantly visible visually.

Correlation Is Mistaken For Causation: The terms correlation and causation are sometimes used interchangeably. Eye color, for example, may be a possible signal of alcohol dependence among European Americans, according to a new paper in the American Medical Genetics Part B Journal: Neuropsychiatric Genetics. But, not unexpectedly, news headlines tended to leave out one or more of the conditional phrases, resulting in headlines like “This Eye Color Is Linked To Alcoholism,” which were both decisive and deceptive. The same dynamic frequently occurs in organizations, sometimes on purpose, but more often because subtleties are ignored or neglected.

Statistical Significance Trumps Thinking: Statistical significance is necessary, but not all statistically significant data is relevant. Therefore, statistical significance should be considered a filter to find potentially useful variables.

Explanation Adds Distortion: Using vocabulary that everyone in the audience comprehends is the simplest way to express results accurately. However, while jargon might make a presentation sound more scientific, it can also confuse the audience to whom the results are delivered.


Some people and organizations use a top-down approach to data analysis, focusing on the business problem they’re trying to address and discovering previously relevant variables in the same or comparable context. Others use a bottom-up approach, which means they try to link variables to the problem they seek to solve (such as website conversions or sales). The risk of the latter approach is that certain statistically significant correlations are an artifact of how the data was examined rather than being an accurate reflection of underlying relationships.

One of statistics’ golden rules is that correlation does not imply causation. Just because two variables’ motions track each other closely across time doesn’t mean one drives the other. Therefore, when conducting market research or analyzing client feedback, it’s important to consider several interpreting of the data. Also, don’t be reluctant to double-check key data details.

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