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The terms Type I error and Type II error are used to describe possible errors made in a statistical decision process.
Jerzy Neyman and Egon Pearson theorized the problems associated with "deciding whether or not a particular sample may be judged as likely to have been randomly drawn from a certain population" and identified "two sources of error", Type Error I : reject the null hypothesis when the null hypothesis is true, and Type Error II : fail to reject the null hypothesis when the null hypothesis is false.
Type I Error in inferential reasoning or statistics, is rejecting a hypothesis when it is true and should be accepted. The probability of making such a mistake is indicated by the level of significance used, so the probability of this Type 1 error can be controlled by altering the level of significance.
Type II error in inferential reasoning or statistics, is accepting a hypothesis when it is false and should be rejected. Also known as false positive.
Type I error and Type II error are linked, however, so that reducing one increases the other. Researcher will try to achieve some balance between Type I error and type II error or alter the balance to meet the needs of a specific situation.
With any decision there are two possible mistakes that can be made. The first mistake is called the Type I error and is described as the situation where one would use a product that does not provide a response above breakeven. The second mistake is called type 2 error and is the situation where the decision maker fails to use a product that would provide a response above breakeven.
In general, there are two different types of error that can occur when making a decision: the first kind (Type I error) are those errors which occur when we reject the null hypothesis although the null hypothesis is true. The second kind (Type II error) of errors arise when we accept the null hypothesis although the alternative hypothesis is true.