# What causes statistical methods to be not reliable?

These can occur if the underlying assumptions of the analyses are not met, the wrong values are used in calculations, statistical code is misspecified, incorrect statistical methods are chosen, or a statistical test result is misinterpreted, regardless of the quality of the underlying data.

## How can statistical data 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.

## For what reason might statistics be misused?

Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental.

## What are the disadvantages of statistical data?

• Qualitative Aspect Ignored:
• It does not deal with individual items:
• It does not depict entire story of phenomenon:
• It is liable to be miscued:
• Laws are not exact:
• Results are true only on average:
• To Many methods to study problems:

## What are the distrust of statistics?

Distrust of statistics means lack of confidence in the statistical methods and statements. As statistics suffers from various limitations, that is the reason statistics become a thing of distrust. ACCORDING TO YULE AND KENDALL. “Statistical methods are most dangerous tools in the hands of an inexpert.”

## What are the types of errors in statistics?

Error (statistical error) describes the difference between a value obtained from a data collection process and the ‘true’ value for the population. The greater the error, the less representative the data are of the population. Data can be affected by two types of error: sampling error and non-sampling error.

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

In 2007, toothpaste company Colgate ran an ad stating that 80% of dentists recommend their product. Based on the promotion, many shoppers assumed Colgate was the best choice for their dental health. But this wasn’t necessarily true. In reality, this is a famous example of misleading statistics.

## What is a misinterpreted statistic?

What Is A Misleading Statistic? Misleading statistics refers to the misuse of numerical data either intentionally or by error. The results provide deceiving information that creates false narratives around a topic. Misuse of statistics often happens in advertisements, politics, news, media, and others.

## How do you know if statistics are misleading?

1. The omission of the baseline or truncated axis on a graph.
2. The intervals and scales. Check for uneven increments and odd measurements (use of numbers instead of percentages etc.).
3. The complete context and other comparative graphs to see how similar data is measured and represented.

## How can statistical data be abused?

However, statistics can be abused too. The following lists some ways in which this frequently happens: Quoting statistics based on non-representative samples. Choosing the “average” value for a sample which most lends itself to your position, when a different “average” value would be more appropriate.

## What are the 5 limitations of statistics?

Statistics deal with groups and aggregates only. 2) Statistical methods are best applicable to quantitative data. (3) Statistics cannot be applied to heterogeneous data. (4) If sufficient care is not exercised in collecting, analyzing and interpreting the data, statistical results might be misleading.

## What are the three limitations of statistics?

• (1) Statistics laws are true on average.
• (2) Statistical methods are best applicable to quantitative data.
• (3) Statistics cannot be applied to heterogeneous data.
• (4) If sufficient care is not exercised in collecting, analyzing and interpreting the data, statistical results might be misleading.

## What are the main cause of distrust of statistics?

Following are the main reasons for distrust of statistics : (i) Figures are manipulated by dishonest persons to present a wrong picture of the facts. (ii) People do not really know about statistics. (iii) People have blind faith in statistics.

## What are the reasons of distrust in statics?

• Incomplete knowledge of statistical methods.
• Unrealistic assumptions.
• Deliberate misuse of statistics.
• Ignoring limitations of statistics.
• Misuse of statistics.

## What are the limitations and distrust of statistics?

Limitations of Statistics Statistics deals only with quantitative data and not the qualitative and descriptive facts like efficiency, intelligence, honesty, blindness, etc.

## What type of error is fatal to statistical results?

Type I errors in statistics occur when statisticians incorrectly reject the null hypothesis, or statement of no effect, when the null hypothesis is true while Type II errors occur when statisticians fail to reject the null hypothesis and the alternative hypothesis, or the statement for which the test is being conducted …

## What are errors usually found in testing a hypothesis?

In the framework of hypothesis tests there are two types of errors: Type I error and type II error. A type I error occurs if a true null hypothesis is rejected (a “false positive”), while a type II error occurs if a false null hypothesis is not rejected (a “false negative”).

## What type of errors are generally involved in analytical data?

Three general types of errors occur in lab measurements: random error, systematic error, and gross errors. Random (or indeterminate) errors are caused by uncontrollable fluctuations in variables that affect experimental results.

## Why do statistics lie?

Obvious lies are often caused by ignoring the characteristics of the sample distributions such as shape, symmetry and variability of the data. They can usually be avoided by plotting the data and using analytical tools to test the assumptions before selecting the correct methods.

## How do you lie in a statistical summary?

Darrell Huff’s book is about the long history of data deception. He explains the many ways data can be manipulated — to misrepresent facts, to tell a different story — in advertising, politics, and other areas and how to defend yourself from it.

## Can statistics be manipulated?

There are several undeniable truths about statistics: First and foremost, they can be manipulated, massaged and misstated.

## Why is it so easy to lie with statistics?

When numbers appear, the reader believes some truth is about to be imparted. Even a nonsensical statement such as this carries the air of authority until the meaning sinks in. Yes, using statistics to lie is easy – as you will soon see. And, statistics are a valid and useful tool.

## What could happen if the data is misinterpreted?

Important Variables Are Omitted A single missing variable can cause data to be misinterpreted. And when data is misinterpreted, it leads to faulty conclusions and sometimes unwise investments.

## What are some common misuses of statistics?

• Apples & Oranges. Comparing things that are not comparable or using unfair or impractical criteria of comparison.
• Biased Labeling. Misleading labels on a graph.
• Biased Samples.
• Cognitive Biases.
• Correlation vs Causation.
• Data Dredging.
• Estimation Error.
• Garbage In Garbage Out.

## Which of the following is are limitations of statistics?

Statistics only deals with quantitative data it does not deal well with qualitative data beauty, honesty, goodwil etc cant be measured. The laws of statistic are not exact and they might be used improperly to misinform. Political parties may create misleading statistics to gain favor with the people.