*“There are three kinds of lies: lies, damned lies, and statistics.” ~ Mark Twain.*

Whenever you read about the misuse of statistics, you might be thinking statistics can misguide you. *If yes, then why do people do this, and how is it possible?*

The below picture has explained how the data is misleading you.

You might be thinking about how people can mislead you with statistical data. Let’s test it on you by answering the below question.

**Task: **Just look at the picture once and do not count the number of animals. Can you tell me which animal is preferred as the best pet animal by the people?

**Solution: **Some of you might answer that “dog” is the most preferred pet animal. But if you look at the picture again and count the number, then you will find that “cat” is the most preferred pet.

This is an example of how the misuse of statistics with data visualization is done.

**Then, why is statistics still in demand?**

Well, it is known to all that statistical analysis plays an important role in the field of business, government funding, and much more.

With the increase of global actions and advancement in technology, statistics has even more significance in analyzing various marketing strategies for businesses and much more.

The statistical data can help in solving various uncertainties of the business, help in better decision-making, make necessary judgments, and give more weightage to evidence.

Apart from this, the statistics data is essential for proper planning so that a business can stand out effectively.

As the demand for statistics increases in this digital era, simultaneously, software and other advanced technologies are responsible for leading to the **misuse of statistics**.

Yes, it is true that several people can misuse statistics information for their personal benefits.

It might not be very clear to you. Let’s check first what exactly misuse of statistics is.

**What is the misuse of statistics?**

**Misuse of statistics **can be defined as the misuse of numerical information and misguiding people with certain details.

When someone misuses the data, he/she can use it for *his/her personal benefit, creating a bad image about anything, or for other purposes. *

*The incorrect information, or error in detail, or not providing full detail about the topics* are some of the **examples of misuse of statistics**.

**What is an example of using statistics to mislead?**

In 2007, the Colgate company came up with an ad that shows that 80% of the dentists are recommending their product to solve dental problems.

As per details of the promotion, several shoppers supposed Colgate as the best choice. But actually, it was not true. Therefore, it considers being a popular example of statistics to mislead.

Key Point:What is misuse of data?The misuse of data occurs when the information is used in a way it was not supposed to be. The user agreements, industrial documents, and corporate policies can be used for misuse of the data. |

**What are some common misuses of statistics?**

There are various types of statistics misusage, but below is the list of the most common misuse of statistics. And these are:

Biased labeling. | Faulty polling. |

Estimation error. | Data fishing. |

Out of context data. | Intentional and selective bias. |

Regression towards the mean. | Prosecutor’s Fallacy. |

Flawed correlations. | Significance. |

Misleading data visualization. | Tyranny of averages. |

Using percentage change in sequence with a small sample size. | Biased samples. |

**How do the statistics data misuse?**

Keep in mind that people can misuse the statistics data by accident or by purpose. There are several ways to misuse statistics in which certain parameters can be changed, or studies can be modified to represent the wrong information to the people.

Here some of the mishaps that are responsible for the **misuse of statistics**:

*Flawed correlations*

*Flawed correlations*

The major issue with the correlation is that the collected or analyzed data seems correlated or interrelated when it is taken on a large scale.

The collected data can easily be manipulated to show that there is a correlation between the collected data, but it actually does not exist.

**To understand this, let’s take an example of it.**

A person has collected the details about the increase in car accidents in a particular area of the USA in May month (A), an increase in bear attacks in the USA’s same area in May month (B).

This detail can have six different possible cases:

- Car accident (A) cause bear attack (B),
- Car accident (A) & bear attack (B) partially cause one another,
- Bear attack (B) which is caused by any third parameter (C) which relates to a car accident (A),
- Bear attack (B) causing car accident (A),
- Car accident (A) & bear attack (B) because of a third parameter (C),
- The relationship is the only cause.

A sensible individual can easily define that* a car accident does not cause any bear attack.* Many car accidents can result from any other parameter, such as an increasing population and much more.

At the same time, *bear attacks can be increased* because of the high rate of tourists in May month or any other reason.

*You might be thinking, what is the point in this example.*

It is clear that the bear can be the cause of the car accident, so a bear can be correlated with this.

That is why the statistical data can be ** misused **to show that the bear is responsible for most car accidents.

**SOLUTION: **Certain strict actions can be taken against the bear companies and drink and drive policy.

This is how the **misuse of statistics** can lead to a change in the collected data.

*Misuse of data visualization*

*Misuse of data visualization*

The data visualization represents the collected data in the form of different charts and graphical representations for the different groups of elements.

It does not matter what kind of data visualization an individual uses; the graph must convey the details of the “used scale” with its starting point, the technique used to calculate the data (like time or dataset).

The absence of graphical data elements can lead to inadequate visual data representations with respect to defined factors like data visualization errors.

It is necessary to identify the intermediate data points when it is used to add value to the specific information; otherwise, it will misguide the people.

Besides this, it is important to increase the reliability of different intelligent automation solutions for several data points as it helps the individuals to believe the analysis.

**Let’s take an example to understand it:**

In the below example, you might be thinking that KFC Chicken twister shows the calories ratio as half of Wendy’s version. But if you check the scale and calculate the actual difference, it is just 70 calories difference. Doesn’t it look interesting!!!

**SOLUTION:**** **The best and technical practices (that is, scaling or design) must be implemented to compare the information collected from various datasets, locations, sources, and times.

With the help of comparing the data, the **misuse of statistics** can be minimized greatly.

*The change in percentage combination within the small sampled value*

*The change in percentage combination within the small sampled value*

This can be another method for analyzing the **misuse of statistics **data, which is linked with collected sample value options with respect to the given sample size.

When the collected data or survey data do not match with the sample size or seem unstable and represented with the percentage value, it can easily misguide the statistics figure.

**Let’s take an example of it.**

Ask some questions to the 20 people and get 19 answers to “yes” (which means 95% answer as yes). At the same time, asking the same question to the 1,000 individuals and getting “yes” from 950 people gives the result as 95%.

*This shows that the percentage value’s validity is not the same, although the result is seen as 95%. *

Therefore, it can be examined that the percentage answer is not suitable to represent the small statistics data because asking a question to the small group and a large group can make a huge difference in the analysis.

**What are the common mistakes made during interpreting data? **

There are several mistakes made at the time of the data interpretation. I have mentioned the most common mistakes that can lead to misleading or misuse of statistics. Let’s check those mistakes.

- Ignoring the uncertainty of the collected data or numbers.

- Mixing up linear and logarithmic scales. It will make a huge difference in the graphs of the statistics data.

- Estimating correlation implies causation. It states that relations like X cause Y, Y cause X, and so on.

- Overlooking regression value to the mean.
- Use the value of relative change while absolute changes are more meaningful.

**Take a look at some of the interesting misleading statistics!!**

**So, what are the questions that help you to make data sensible?**

Below are the questions you can ask yourself by visualizing and checking whether the data is misleading.

Do the graphical axes start from 0? |

If there is a pie chart, does the proportion sum equal 100%? |

The graphical data labeled correctly or not. |

Is the scale logarithmic or linear? |

Is the graphical format the best way to know the collected data? |

**Conclusion**

The above three points about the **misuse of statistics** information explain that it is necessary to analyze the given information. Otherwise, it can misguide the individuals and easily create a huge difference with the real value.

Although, statistics studies are necessary to make better decisions for the growth of businesses, analyze the positive impact of government policies, and much more.

You have now understood how the statistical data can mislead you, so always examine the data seriously before making any conclusion about the given or surveyed details; otherwise, it can lead to some mishaps.

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**Frequently Asked Questions**

**How are statistics abused?**

The wrong application of statistical tests, incorrect or incomplete multivariate model building, lack of transparency, exclusion of outliers, and disclosure about decisions are considered data abuses.

**Can statistics be manipulated?**

There are variously defined truths related to statistics. The first one is that the data can be easily manipulated, misstated, and massaged. The other is that if the false statistical data is repeated often, it is considered valid.

**What is a statistical fact?**

Statistics are those facts that are taken from analyzing information, which is expressed in numbers. For instance, details regarding the number of times in which something occurs. Like official statistics say that real wages are declining by 22%. It is not reliable or relevant statistics for the office employees as it does not include the workload of each employee and many other factors.