What is a common mistake in using statistics?

Types of mistakes Expecting too much certainty. Misunderstandings about probability. Mistakes in thinking about causation. Problematical choice of measure.

What is a misleading statistic?

Misleading statistics, on the other hand, is a term that refers to the misusage of numerical data, either intentionally or due to error, that results in misleading information. Misleading statistics can deceive the receiver of the information if the receiver is not careful to notice the error or deception.

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 are the types of statistical errors?

Two potential types of statistical error are Type I error (α, or level of significance), when one falsely rejects a null hypothesis that is true, and Type II error (β), when one fails to reject a null hypothesis that is false.

What are the causes of statistical error?

The second axis distinguishes five fundamental sources of statistical error: sampling, measurement, estimation, hypothesis testing, and reporting. Bias is error of consistent tendency in direction. For exam- ple, an assay that consistently tends to underestimate concen- trations of a metabolite is a biased assay.

What are the limitation 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 some misuses in statistics?

A few of the fallacies are explicitly or potentially statistical including sampling, statistical nonsense, statistical probability, false extrapolation, false interpolation and insidious generalization.

What is statistical error example?

For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the “error” is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the “error” is −0.05 meters.

Why do statistics have a bad reputation?

There are three basic reasons that people are bad at statistics. One: They are not educated in statistics. Two: They have fixed beliefs that discourage acceptance of evidence. Three: They know statistics, and deliberately manipulate statistics in accordance with their own purposes.