Total Pageviews

Followers

Search This Blog

Wednesday 29 April 2020

Difference between Type I and Type II Error

The statistical practice of hypothesis testing is widespread not only in statistics but also throughout the natural and social sciences. When we conduct a hypothesis test there a couple of things that could go wrong.  There are two kinds of errors, which by design cannot be avoided, and we must be aware that these errors exist. The errors are given the quite pedestrian names of type I and type II errors.
      The first kind of error that is possible involves the rejection of a null hypothesis that is actually true. This kind of error is called a type I error and is sometimes called an error of the first kind. On the other hand the other kind of error that is possible occurs when we do not reject a null hypothesis that is false. This sort of error is called a type II error and is also referred to as an error of the second kind.      The differences are stated as under:-
1)      Hypothesis: Null hypothesis is related to type  I error and Alternate hypothesis is related to Type II error.  In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis, while a type II error is the non-rejection of a false null hypothesis.
2)      Occurrence: A type I error, also known as an error of the first kind, occurs when the null hypothesis (H0) is true, but is rejected. Type II error, also known as an error of the second kind, occurs when the null hypothesis is false, but erroneously fails to be rejected. Type II error means accepting the hypothesis which should have been rejected.
3)      Effect: Type I errors are equivalent to false positives. Type II errors are equivalent to false negatives.
4)      Decision based on belief : A Type I error occurs when we believe a falsehood. Type II error is committed when we fail to believe a truth.
5)      Control: Type I errors can be controlled. The value of alpha, which is related to the level of significance that we selected has a direct bearing on type I errors.
6)      Rate of error:  The rate of the type I error is called the size of the test and denoted by the Greek letter α (alpha).It usually equals the significance level of a test. If type I error is fixed at 5 %, it means that there are about 5 chances in 100 that we will reject H0 when H0 is true. The probability of a type II error is given by the Greek letter β (beta). This number is related to the power or sensitivity of the hypothesis test, denoted by 1 – beta.
7)      Error avoidance: Type I and type II errors are part of the process of hypothesis testing. Although the errors cannot be completely eliminated, we can minimize one type of error. If we try to minimize one the other increases and both are inversely related to each other.
8)      Testing used: Prescriptive testing is used to increase the level of confidence, which in turn reduces Type I errors.  Descriptive testing is used to better describe the test condition and acceptance criteria, which in turn reduces Type II errors.
9)      Level of confidence The chances of making a Type I error are reduced by increasing the level of confidence.
Many statisticians are now adopting a third type of error, a type III, which is where the null hypothesis was rejected for the wrong reason.  In an experiment, a researcher might assume a hypothesis and perform research. After analyzing the results statistically, the null is rejected. The problem is, that there may be some relationship between the variables, but it could be for a different reason than stated in the hypothesis. An unknown process may underlie the relationship.
(Source: Reference material in Research Methodology in Social Science and search engines)

1 comment: