Whenever working with statistical tests there is a chance that the conclusion from the test could be wrong. We could accept a false null hypothesis and we could reject a correct null hypothesis. Hence, in order for decision rules or tests of hypothesis to be good, they must be designed so as to minimize the errors of decision. For a given sample size this is a difficult task, since any attempt to minimize one of them results in increasing the other kind. The only way to reduce the chance of both types is by increasing the sample size. The two types of errors mentioned above are referred to as the type I error and the type II error.

The type I error

The probability to reject a correct null hypothesis.

The type II error

The probability to accept a false null hypothesis

An additional concept related to the two types of errors is the so called power of the test. The power of the test is the probability to identify a false null hypothesis. It is always the case that we would like the power of the test to be as large as possible. However, since we need to know the true value of the population parameter we will never be able to calculate the type II error and the corresponding power of the test in practice. But for a given sample we must remember that the smaller we choose the significance level, the larger become the type II error and the smaller become the power of the test.

The power of a test

The probability to reject a false null hypothesis

Table 4.2 Errors in hypothesis testing

Example 4.2

To better understand the mechanism of the two types of errors we will consider an example were we calculate the probabilities for each cell given in Table 4.2. Let us use the regression results from Example 4.1 and focus on the slope coefficient.

In that example we had a significance level of 5 percent. It was our choice, and hence we have specified that the probability to reject a correct null hypothesis should be at most 5 percent. Given the significance level we can calculate the interval that will be used as a decision rule for our test. If the estimated parameter is within the interval we will accept the null hypothesis, and if it is located outside the interval we will reject the null hypothesis.

In Example 4.1 the null hypothesis stated that the population parameter equals zero. Together with the estimated standard error we know the distribution of the estimated parameter when the null hypothesis is correct. That is

Hence, an interval that covers 95 percent of the distribution is given by the endpoints of the confidence interval. For this particular case we have the following interval:

Therefore, if our estimator comes from a distribution with mean zero, there is a 95 percent chance that it will be located in the above mentioned interval. The interval can therefore be seen as a decision rule. If the estimated parameter value takes a value within this interval, we should conclude that it comes from a distribution with mean zero. Since we decided about the significance level we know the probability of the type I error, since they always coincide. We will therefore move on and calculate the type II error.

In order to be able to calculate the type II error we need to know the true value, that is, we need to know the population value of the parameter. That will never happen in reality, but by assuming different values one can receive a picture of the size of the possible chance of decision error. In this example we will assume that we know the value and that it equals 0.25. Given this value we have a new distribution for our estimator that we will use when calculating the probability of a type II error, namely:

This is the distribution related to the alternative hypothesis. In order to find the probability of a type II error we simply calculate how large part of this distribution that overlap the region of our decision rule given by interval (4.10). That is, we have to calculate the following probability using the distribution given by (4.11):

In order to calculate this probability we have to use the t-value transformation and use the table for the t-distribution to find the probability. The following steps need to be done:

Hence, with this setup there is a 78 percent chance of committing a type II error. The only way to reduce this probability is to increase the number of observations in the sample. If that is not possible you are stuck with a problem. Observe that if you decide to decrease the probability of the type I error further, the interval given by (4.10) will be even wider. When that happens, a larger portion of the true distribution will be covered, and hence increase the type II error.

Once the type II probability is calculated it is straight forward to calculate the power of the test. In this case the power of the test would be (1 - /3) =1-0.7821=0.2179. Hence, there is only a 22 percent chance to reject a false null hypothesis.

Found a mistake? Please highlight the word and press Shift + Enter