Run test of randomness is a non parametric test that is widely used to test the randomness of a sample. While run test of randomness is a sufficient test, it does not necessarily give an exact result in all cases. For example, this can be used in the stock market. If we want to test whether prices of a particular company are behaving randomly or if there are any patterns in the price of that company, we can use the run test of randomness. Or, in another case, if we want to test whether or not a sample is independent of each other or if the sample has any pattern, we can use the run test of randomness. Thus, the test for such problems is called the Run test of randomness.
Key concept and terms:
Run: Run is basically the newly assigned value to a part of a particular series. For instance, if in a sample M= male and F=female, the first 22 responses in that sample might come as MMMMFFFMFFFFMFFFFMMMFF. Starting from MMMM and ending with FF, there are 8 runs in this example. Basically, run test for randomness assumes binary value for that particular series. Run test for randomness assumes that the value of the binary variable must be equal to 2 binary values or more than 2 values. In SPSS, run test of randomness can test many values in a single time but that value must be numeric or we should convert them into numeric form.
Run Test: Run test is based on the law of probability. Run test of randomness can be performed in SPSS very easily. SPSS computes observer run and gives a critical value for run. We can compare that observed value with the computed critical value. SPSS shows two tailed test value by default. For a small sample, binary variable exact test is available to test its significance. For the larger sample, Monte Carlo estimation gives the significant value to test its randomness.
Cut Point: The algorithm of Run test for randomness divides the series through cut points. We can select cut point mean, median, or mode to specify as a custom point.
Type of significance estimate: Run test of randomness significance can be easily tested by using an exact button in SPSS available in run test.
Assumptions in Run test of randomness:
1. Data order: run test of randomness assumes that data is entered in order (not grouped).
2. Numeric data: Run test of randomness assumes that data is in numeric form. This is a compulsory condition for run test, because in numeric form, it is easy to assign run to that particular value.
3. Data Level: In run test or randomness we assume that data should be in order. But if data is not in ordered form, then the researcher has to assign a value. These values are one of the following: mean, median, mode or a cut point. By assigning one of these values, data can be ordered.
4. Distribution: Run test of randomness is a non-parametric test. Hence this test does not assume any distribution like any other parametric test.
Run Test in SPSS: Run test in SPSS is available in a non-parametric test in the analysis menu. By selecting this option, we will drag the variable in to the test variable list and select the “cut point” option. After clicking the “ok” button, the result will come in front of us. By examining the significance value, we can accept or reject the null hypothesis.
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