“Lies, damned lies, and statistics.” Twain.

You could assume statistics can mislead you when you learn about statistics misuse. Is this true, and how is it possible?

The image below shows how data can deceive you.

You may be considering how statistical data can be misled. Test it by answering the following question.

Simply look at the image once and don’t count the animals. Can you tell me which animal is the most popular as a pet?

Some of you may say “dog” is the most popular pet animal. But if you look at the picture again and count the numbers, the most preferred pet is “cat”.

This is a misuse of statistics with data visualization.

So why are statistics still needed?

We all know that statistical analysis is vital in business, government funding, and many other fields.

With the rise of global activity and technological improvement, statistics is becoming increasingly important in analyzing corporate marketing strategies.

Statistical data can help solve business uncertainties, improve decision-making, make necessary decisions, and give evidence greater weight.

Aside from that, statistical data is required for appropriate planning to make a business stand out.

As the demand for statistics grows in the digital age, software and other technological technologies are accountable for their misuse.

True, some people can use statistics for personal gain.

Maybe it’s not clear to you. Let us first define statistical misuse.

What is statistical misuse?

Misuse of statistics is the misuse of numerical data to mislead people.

When data is misused, it might be used for personal gain, to harm others, or for other goals.

Misuse of statistics includes supplying erroneous information, omitting details, or not providing enough information.

What is an example of statistical fraud?

In 2007, Colgate created an advertisement showing that 80% of dentists prescribe their product to treat tooth issues.

According to the promotion’s specifics, many customers chose Colgate. But it wasn’t true. Thus, it is a popular example of statistics that mislead.

Point:

Misuse of data

Misuse of data occurs when data is used in ways it was not intended. Data can be misused via user agreements, industrial documents, and business rules.

What are some common stats misuses?

There are many ways to misuse statistics, but here is a list of the most prevalent. So:

Labeling bias.

Poor polling.

Error estimation

It’s a data

Unrelated data.

Bias with purpose.

Mean regression.

Conjurer’s Fallacy

Correlations err.

Significance.

False data visualization

Averages tyranny.

Utilisation of tiny sample size % change.

skewed data.

Statistical Data Misuse:

People can misuse statistical data by accident or design. Statistics can be manipulated to offer incorrect information to the public in numerous ways.

Here are some examples of misused statistics:

Correlations err

The main difficulty with correlation is that enormous amounts of data seem to be connected or interrelated.

The obtained data can be easily altered to suggest a correlation between them, which does not exist.

Exemplifier illustrative

• An increase in car accidents in a particular area of the USA in May (A); • An increase in bear attacks in the same USA area in May (B) (B).

This detail has six possible cases:

BEAR ATTACK CAUSED BY CAR (B)

,

• Car accident (A) and bear attack (B) are related.

• A bear attack (B) caused by a third parameter (C) involving an automobile accident (A),

× Bear attack causes vehicle crash (A)

,

• Bear attack (B) and car collision (A) (C),

• Only the relationship causes.

A rational person knows that a car collision does not provoke a bear attack. Increasing population, for example, can cause many car accidents.

At the same time, bear assaults may increase due to high tourist numbers in May.

You may wonder, what’s the point of this example?

Clearly, a bear can cause an automobile collision, thus this is related.

That’s why statistics can be twisted to suggest that bears cause most car accidents.

SOLUTION: Bear corporations and drink and drive policies can be severely punished.

This is how misusing statistics can alter obtained data.

Data visualization abuse

The data visualization displays the collected data in various charts and graphs for various element groupings.

No matter what data visualization method is employed, the graph must express the “used scale” with its starting point, and the data calculation approach (like time or dataset).

Data visualization problems can occur when there are no graphical data items.

Otherwise, individuals will be misled if the intermediary data points are not identified.

Also, increasing the trustworthiness of various intelligent automation solutions for various data points helps people believe the analysis.

To comprehend it, consider this:

You could imagine that the KFC Chicken Twister has half the calories of Wendy’s version. But if you weigh yourself and compute the difference, it’s only 70 calories. It seems intriguing!!!

SOLUTION: Use best and technical practices (such scaling or design) to compare data from different datasets, locations, sources, and timeframes.

The misuse of statistics can be considerably reduced by comparing data.

Within the limited sampled value, the percentage change

This is another way for examining the misuse of statistics data, which is linked to collected sample size options.

When collected data or survey data do not match the sample size or appear unstable, the statistics figure can be easily misled.

Let us use an example.

Ask some questions and get 19 “yes” answers (which means 95 percent answer as yes). Similarly, asking the identical question to 1,000 persons and getting 950 yeses provides the result as 95%.

The outcome is 95 percent, however the percentage value is not the same.

It can be seen that using a percentage to represent small statistics data is not appropriate, as the size of the group can greatly affect the analysis.

What are common interpreting errors?

Several errors occur during data interpretation. I’ve included the most typical errors that lead to statistical misinformation. Let’s fix those errors.

• Ignoring the ambiguity of the collected data.

|||||||||||||||| It will make a big difference on the statistics graphs.

Causation is implied by correlation. It states that X causes Y, and so on.

• Ignoring regression to the mean.

• Relative change is more meaningful than absolute change.

Check out these intriguing misleading statistics!!

So, what questions assist you make sense of data?

Here are some questions to ask yourself when visualizing data.

Do the axes start at 0?

Does a pie chart’s proportion add to 100%?

Graphs accurately labeled or not.

Is it linear or logarithmic?

Is a graphical representation of the data best?

Conclusion

The previous three reasons concerning misusing statistics data show that data must be analyzed. Otherwise, it can easily mislead people and distort their perception of worth.

However, statistics studies are required to make better corporate decisions, assess the influence of government programs, and much more.

You now know how statistical data can deceive you, so always study the facts carefully before drawing any conclusions about the given or inspected material.

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Misuse of statistics

Data abuses include erroneous or insufficient multivariate model development, lack of transparency, exclusion of outliers, and failure to disclose decisions.

Can stats be tampered?

Statistics has many different truths. The first is that data can be easily misrepresented or distorted. Another is that repeated erroneous statistical data is regarded valid.

What is a stat?

Statistics are facts derived through examining data and expressed numerically. For example, the number of times something occurs. Official statistics show a 22% decline in real wages. It is neither dependable or meaningful for office personnel because it does not incorporate their workload and other aspects.