One popular example from the news is the Terri Schiavo case , a right-to-die legal case in the U. A glance at this graph suggests that when compared to Republicans and Independents, 3 times more Democrats agreed with the court. The truncated graph and tampered Y-axis starting at 50 instead of 0 distort the data, and lead you to believe an exaggerated idea about a certain group.
The intervals and scales. Check for uneven increments and odd measurements use of numbers instead of percentages etc. The complete context and other comparative graphs to see how similar data is measured and represented. They create shocking headlines that attract swarms of traffic but provide flawed insights at best.
Instead of helping you navigate through detours, potholes and pitfalls, they knowingly- or unknowingly - steer you right into them. Research is expensive and time-consuming. Check who is sponsoring it, weigh their bias on the topic and how they might benefit from results. Are they a B2C company with a product? A consulting service? An independent university-funded study?
Are the scales and intervals evenly spaced and neutral? Is a statistic pushing a specific idea or agenda? Are there too many metrics in your dashboard? To prevent misleading statistics and data from polluting your dashboards, reports, and analytics , greet new information with a curious and skeptical attitude.
He loves all things content marketing. If you tested 10, people, that percentage is a pretty convincing reason to develop that version. But if you tested only 20 people, that means only 12 are interested in the idea.
Twelve is too small a group to show you that the new feature will be worth your investment. Small group sizes can also lead to biased sampling. The smaller the group, the less likely it is to represent the diverse demographics of your customer base. An ideal sample size depends on many factors, like your company and the goals for your project. Using a third-party tool helps you reliably assess your sample size without having to figure out the calculations on your own.
Users enter their expected conversion rate and the percent change they are looking for. The way you word survey questions can also be a source of misleading statistics.
A recent UK study shows that the way you phrase a question directly affects how a person answers. One example is survey questions that ask for confirmation. Essentially, you are including the answer in the question. Check your surveys for manipulative wording that might lead respondents to give a particular answer.
A few examples of influential phrasing include:. Check for leading language by asking co-workers to review surveys before sending to customers. Ask what parts of your questions, if any, suggest how they should respond. Confirmation bias is when you have a set result you want or expect to see, so you look at only data that affirms your belief. Companies are most susceptible to this phenomenon when a single employee is giving a presentation. Whether the employee realizes it or not, they may not be providing a full picture of the data due to their own views—and that can lead to poorly informed decisions.
To support her claim, she shows that very few customer support calls mention this feature. As it turns out, she was looking at calls from only the last six months. When analyzing support calls from long-term customers, the product team sees a much higher percentage bringing up issues with the Favorites feature.
Everyone has unconscious biases. But not everyone has the same ones. If you switch the given, the probability can change by a lot. Thereby, there was 1 in 73 million chance that Sally was innocent. Among some other overlooked factors, based on this argument, Sally Clark was convicted. The statistician later showed that chances of Sally's innocence were two in three if the data was reversed to sudden unexpected deaths.
When looking at statistics, consider the source of data; whether its sampled or controlled experiment and find all the other factors that tie to the analysis. Look for all the tricks used in the distortion of the truth to deliberately direct others towards a preconceived target. Make sure the data is accurate , and the truth is the highest priority whether you are a viewer or you are the one collecting the data. Wendy is a data-oriented marketing geek who loves to read detective fiction or try new baking recipes.
She writes articles on the latest industry updates or trends. Enter your email and get curated content straight to your inbox! Pinky promise. Log in Try it free. Resources A collection of useful resources to help power up your performance monitoring and reporting flow.
Thank you for subscribing! It's great to feel loved. A good rule of thumb is to always take polling with a grain of salt, and to try to review the questions that were actually presented. They provide great insight, often more so than the answers. The problem with correlations is this: if you measure enough variables, eventually it will appear that some of them correlate. As one out of twenty will inevitably be deemed significant without any direct correlation, studies can be manipulated with enough data to prove a correlation that does not exist or that is not significant enough to prove causation.
Any sensible person would easily identify the fact that car accidents do not cause bear attacks. Each is likely a result of a third factor, that being: an increased population, due to high tourism season in the month of June. It would be preposterous to say that they cause each other It is easy to see a correlation. But, what about causation?
What if the measured variables were different? Clearly there is a correlation between the two, but is there causation? Many would falsely assume, yes, solely based on the strength of the correlation. Tread carefully, for either knowingly or ignorantly, correlation hunting will continue to exist within statistical studies.
It is a data mining technique where extremely large volumes of data are analyzed for the purposes of discovering relationships between data points. Data dredging is a self-serving technique often employed for the unethical purpose of circumventing traditional data mining techniques, in order to seek additional data conclusions that do not exist. This is not to say that there is no proper use of data mining, as it can in-fact lead to surprise outliers and interesting analyses.
However, more often than not, data dredging is used to assume the existence of data relationships without further study. Often times, data fishing results in studies that are highly publicized due to their important or outlandish findings. These studies are very soon contradicted by other important or outlandish findings. These false correlations often leave the general public very confused, and searching for answers regarding the significance of causation and correlation. Likewise, another common practice with data is the omission, meaning that after looking at a large data set of answers, you only pick the ones that are supporting your views and findings and leave out those that contradict it.
As mentioned in the beginning of this article, it has been shown that a third of the scientists admitted that they had questionable research practices, including withholding analytical details and modifying results! It becomes hard to believe any analysis! Insightful graphs and charts include very basic, but essential, grouping of elements.
Whatever the types of data visualization you choose to use, it must convey:. Absent these elements, visual data representations should be viewed with a grain of salt, taking into account the common data visualization mistakes one can make.
Intermediate data points should also be identified and context given if it would add value to the information presented. With the increasing reliance on intelligent solution automation for variable data point comparisons, best practices i. The last of our most common examples for misuse of statistics and misleading data is, perhaps, the most serious.
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