Sometimes misleading data is presented with a deliberate intention to manipulate people and to prompt an agenda. Likewise, sometimes, it is due to carelessness or just the matter of not understanding the data properly. It is vital to know the different types of ways statistics can be misused so that you can identify them and do not make decisions based on biased or incorrect data. The data can be misleading due to the sampling method used to obtain data.
For instance, the size and the type of sample used in any statistics play a significant role — many polls and questionnaires target certain audiences that provide specific answers, resulting in small and biased sample sizes.
There are many misleading statistics examples, particularly misleading graphs in the news are quite common. There are many ways to manipulate data, including but not limited to inappropriate use of descriptive statistics. Knowing about them will help you spot them. Following are some misleading stats examples. This kind of data display can confuse and show the difference bigger than it is.
You can identify misleading graphs in the media if you look at the numbers and see how much variation is in the numbers. For example, the Fox News poll compared people on welfare and the number of people with jobs is a prime example of selective data display. It included all people on welfare from a house in which one or more people were on welfare regardless of other members of the family who were not on welfare. To the contrary, only a person on full-time job was included in on job sample.
The graph is not only an example of bad sampling but also exaggerated the difference. Failing to recognise false statistics and data is a threat to data-driven decision making. What is a Misleading Statistic? Source Misleading statistics are created when a fault - deliberate or not - is present in one of the 3 key aspects of research: Collecting: Using small sample sizes that project big numbers but have little statistical significance.
Organizing: Omitting findings that contradict the point the researcher is trying to prove. When two variables correlate the following usually applies: Y causes X. X causes Y. The correlation is down to chance. Misleading Graphs and Visuals Data visualizations turn raw numbers into visual representations of key relationships, trends, and patterns. Avoid being misled when viewing graphs and visuals by looking out for: The omission of the baseline or truncated axis on a graph.
Bad statistics and data are dangerous. Next time you encounter convincing data, run by these simple but powerful questions: Who is doing the research? Can sample size and study length be taken seriously? Inspecting the supporting or veiled numbers will expose weak statistical strength. Are data visuals represented fairly? Is the research represented honestly and in an impartial manner? Review the language used, the way the question is framed, and the people being surveyed. Metric Stack Newsletter New benchmarks, trending metrics content, and tips and tricks to help you level up your analytics.
Delivered to your inbox every week. 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 an employee comes to you with a proposal, have another team member review the project idea and the presentation. Each person will approach the data differently, so someone will likely realize whether the data is skewed toward one perspective. Offer training to help employees become aware of their biases.
This is especially important when it comes to internal hiring and employee development decisions. Training can help teams avoid misleading statistics that could negatively affect business decisions ranging from product features to team diversity. When most people see a data visualization, they immediately draw conclusions about the information.
Unusual graph intervals, for example, can skew results and make them look especially dramatic. The two graphs below present the same data. But the graph on the right starts at zero, and the one on the left starts at 3. The left graph paints a picture of rapidly rising interest rates. Yet in reality, rates rose only a fraction of a percent over five years. Charts and graphs are also misleading when they have irregular intervals. In the chart below, it looks at first glance as though whatever is being measured is rising relatively quickly, but at a fairly steady rate.
The data is actually increasing at an exponential rate. The non-standard y-axis intervals understate the change, and make it appear to be increasing less quickly than it really is. People not only stretch the truth, fib or misspeak themselves. They lie. Ask them a question and, just for the hell of it they may lie. They may lie because they find the truth uncomfortable or embarrassing, or because they simply want to screw up your results.
With lying a virtual social necessity do you really tell your best friend that his or her breath could knock a buzzard off a honey wagon? Finally, many studies not only try to find out what people do, but why they do it. Here the problem lies in respondents' inability to articulate or explain their true feelings and motivations. Many people do things because it "feels" like the thing to do, but they cannot explain what that feeling is or how it arose.
How Were They Asked? It is not only the respondents but the questioners that contribute their own prejudice to the gathering of facts. Two things that are used in surveys and statistical studies are questions and answers. First, let's examine the questions. Researchers generally have an idea what their research is looking for. They thus formulate questions that will illuminate their research, either pro or con. Prejudice can creep in when a researcher unconsciously words questions in such a way that the answers support his or her contention or opinion.
Various questions of this type are leading questions, loaded questions, and double-barreled questions. Leading questions are those that tell the respondent how to answer. Attorneys sometimes use them. For example, "Is it not true that on the night of the 27th you were drunk? Asking instead, "Were you drunk on the night of the 27th? Loaded questions are those that, no matter how they are answered, the respondent loses.
A loaded question appears to ask for a yes or no answer, yet the actual answer may be neither yes nor no. Double-barreled questions are those that ask for more than one piece of information in the same question. For example, "Do you go up or downtown in the afternoon? Another point to be considered is how the questions were worded. It is easy, and often subconscious, for the questioner to word the questions in such a way as to lead to respondent to reply in a certain way.
For example, a survey on whaling could ask, "Should the only three countries in the world that do so, continue to slaughter to extinction the helpless, harmless intelligent giants of the deep?
It is the answers that sometimes cause difficulty for a researcher. The problems lie not only in how the respondents answer, but in how the researcher responds to the answer. He or she must then account for the anomaly. He or she may revamp the original concept or theory, revamp the study, or even ignore the data.
The researcher may fall prey to selective perception seeing only what you want to see or cognitive dissonance rationalizing away anything that doesn't fit into your preconceptions. In addition, how the researcher interprets the words in the questions may be at odds with how the respondents interpreted the words. For example, in a recent survey on the incident of rape on college campuses, the questions used words such as unwelcome sexual advance; the researcher interpreted unwelcome sexual advance as rape, while the respondents could well have been referring to a drunk at a bar making a pass, something that most people would accept as disgusting, but not rape.
The order of the questions can also be a problem. Often, the questions can lead a respondent to answer in a certain way because he or she has answered all the previous questions in the same way. In sales, it's a common technique, that can lead a respondent through a series of yes answers, from "it's a nice day," to "sign here.
Thus "How were they asked? Compared with What? Finally, you need to examine statistics to determine what are the comparisons being drawn and are they relevant and valid. For example, say your topic is gun control. You could find statistics on murder rates with handguns per capita in New York City , London and Tokyo.
Such statistics would show much higher rates in New York than the other two cities. It would therefore appear that gun control is a good idea since guns are controlled in London and Tokyo.
However, such statistics must be suspect, not because they are wrong more people are indeed murdered with handguns in New York City than in London or Tokyo , but because they don't tell the whole story. For instance, New York has an extremely stringent weapons control law the Sullivan Act. Since this is the case, what happens to the argument that control laws work? There must be something else influencing the murder rate. What about the culture? The United States is unlike any other country on Earth.
Its society has a tradition of independence and self-sufficiency, where if you have a problem it is normal for you to take care of it yourself, even if you can't.
It is also a country that used to be called "the melting-pot" but is now known as the "mosaic", with New York City a patchwork of often conflicting cultures, languages, customs and attitudes. Add in the traditions of the old West and " gunslinging " becomes an apparently viable option to solve problems.
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