CandleFocus Glossary Editor
A Type I Error, also known as a False Positive Error, is the incorrect rejection of a true hypothesis or the acceptance of a false hypothesis. It is one of the most common types of errors made in statistical testing and occurs when a researcher incorrectly rejects the null hypothesis, believing it to be false when it is actually true. This type of error is also referred to as a false alarm or an alpha error.
In a type I error, the researcher’s conclusion is based on the wrong idea that the variations in the data are due to a specific cause rather than random chance. For example, a researcher might believe a new drug is effective in curing a certain condition, when in fact it is no more effective than the placebo. The researcher has made a type I error, incorrectly rejecting the null hypothesis (i.e., that the drug is no better than the placebo) and accepting the alternative hypothesis (i.e., that the drug is better than the placebo).
The rate at which type I errors occur is measured by the statistical significance level alpha, which is denoted by the lower-case Greek letter alpha. The standard value for alpha is .05, meaning there is a 5% chance of making a type I error. In this sense, reducing the alpha value to .01 would mean that the researcher has reduced the chances of committing a type I error from 5% to 1%.
In conclusion, a type I error or false positive error is the incorrect rejection of a true null hypothesis. It is characterized by a researcher falsely believing that a new drug is effective in curing a certain condition when it is no more effective than the placebo. Type I errors are measured according to their probability, which is denoted as alpha and generally set to .05. Reducing the alpha value lowers the likelihood that a type I error will be made.
In a type I error, the researcher’s conclusion is based on the wrong idea that the variations in the data are due to a specific cause rather than random chance. For example, a researcher might believe a new drug is effective in curing a certain condition, when in fact it is no more effective than the placebo. The researcher has made a type I error, incorrectly rejecting the null hypothesis (i.e., that the drug is no better than the placebo) and accepting the alternative hypothesis (i.e., that the drug is better than the placebo).
The rate at which type I errors occur is measured by the statistical significance level alpha, which is denoted by the lower-case Greek letter alpha. The standard value for alpha is .05, meaning there is a 5% chance of making a type I error. In this sense, reducing the alpha value to .01 would mean that the researcher has reduced the chances of committing a type I error from 5% to 1%.
In conclusion, a type I error or false positive error is the incorrect rejection of a true null hypothesis. It is characterized by a researcher falsely believing that a new drug is effective in curing a certain condition when it is no more effective than the placebo. Type I errors are measured according to their probability, which is denoted as alpha and generally set to .05. Reducing the alpha value lowers the likelihood that a type I error will be made.