What are the ways by which statistics can be misused?

That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.

How statistics can be misinterpreted?

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.

Can statistics be misused explain with three examples?

Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. The false statistics trap can be quite damaging for the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives.

How can statistical data be abused?

Data abuses include the incorrect application of statistical tests, lack of transparency and disclosure about decisions that are made, incomplete or incorrect multivariate model building, or exclusion of outliers.

What are detached statistics?

Detached statistics means stating a statistical measurement but not giving any basis for comparison. Example: Mean is the average of numbers added up and divided by how many numbers are in the set.

Why are statistics misused?

Statistics are the primary tools for assessing relationships and evaluating study questions. Unfortunately, these tools are often misused, either inadvertently because of ignorance or lack of planning, or conspicuously to achieve a specified result.

What are some examples of misleading statistics?

Here are common types of misuse of statistics:

• Faulty polling.
• Flawed correlations.
• Data fishing.
• Misleading data visualization.
• Purposeful and selective bias.
• Using percentage change in combination with a small sample size.