Test di Wald-Wolfowitz Runs II

The Wald-Wolfowitz runs test assesses whether ordered data follows a random sequence or exhibits a detectable pattern. It evaluates the number of runs—consecutive sequences of similar elements—in the data. If the observed number of runs significantly deviates from expected values, the data is considered non-random, indicating an underlying structure or relationship between variables.

Numerical data are converted by assigning binary signs based on a threshold, typically the median. Values above the median receive a plus sign, while values below receive a minus sign. This binary conversion enables identification of runs within the data. For example, baboon body lengths were compared against a median of 74.5 to create a sequence of signs for analysis.

The null hypothesis states that data follow a random sequence with no underlying pattern. The alternative hypothesis states that data are not random and exhibit a specific order or structure. The test determines which hypothesis the observed number of runs supports by comparing it against critical values from a standard table.

Use critical values from a standard table when both n1 (elements with one characteristic) and n2 (elements with another characteristic) are less than or equal to 20, and the significance level is α = 0.05. When these conditions are not met—such as when n1 or n2 exceed 20 or when using a different significance level—calculate the test statistic z using mean and standard deviation formulas instead.

If the observed number of runs falls outside the critical range, reject the null hypothesis and conclude the data is non-random with a detectable pattern. If the number of runs falls within the critical range, fail to reject the null hypothesis, suggesting the data is likely random. The test is two-tailed, meaning randomness is rejected when runs are either too high or too low.

The runs test analyzes binary data using symbols like plus and minus, categorical data with two categories converted to binary form, and numerical data thresholded at the mean or median. It also applies directly to inherent categorical sequences such as DNA bases (A, T, G, C) without conversion. The test's versatility makes it suitable for various sequential data types.

While the runs test is unaffected by sample size or population distribution, making it versatile for sequential data, it cannot measure the degree or magnitude of randomness. It only identifies whether a sequence is random or non-random. The test detects the presence of patterns but does not quantify how strong or pronounced those patterns are.