A test with a high specificity has a low type I error rate.

This probability is known as α-level or a type I error.

Sexual overperception occurs when a type I error is committed by an individual.

She has shown that the modified procedure leads to greater power at the first step without affecting control of the type I error rate.

Violation of this assumption may lead to an increase in Type I error rates.

A type I error is a false-positive or in layman's terms, playing it safe.

This leads to a large Type I error rate.

A type I error is a false positive.

A Type I error occurs when we believe a falsehood.

A systematic review suggested this indicates possible Type I errors.

A test's probability of making a type I error is denoted by α.

Type I error: "rejecting the null hypothesis when it is true".

Such documents are called false positives (see Type I error).

A fire alarm that later turns out to be a false alarm is a type I error.

With this correction the overall type I error rate should be approximately equal to even when the population is stratified.

This procedure will reduce type I error but will produce a regional loss of statistical power.

Testing a hypothesis suggested by the data can very easily result in false positives (type I errors).

These methods have "weak" control of Type I error.

Any statistical analysis involving multiple hypotheses is subject to inflation of the type I error rate if appropriate measures are not taken.

These issues can result in sampling bias and inflated rates of Type I error.

Doing multiple two-sample t-tests would result in an increased chance of committing a type I error.

The level of significance or α is equal the probability of type I error.

In the case of a simple null hypothesis the size is the only possible probability of a type I error.

Type I errors where the null hypothesis is falsely rejected giving a "false positive".

But this inevitably raises the risk of obtaining a false positive (a Type I error).