If the data for one or more of the samples to be analyzed by a one-way blocked analysis of variance (ANOVA) come from a population whose distribution violates the assumption of normality, or outliers are present, then the ANOVA on the original data may provide misleading results, or may not be the most powerful test available. In such cases, transforming the data or using a nonparametric tests may provide a better analysis.

#### Alternative procedures:

- Transformations: correcting nonnormality and unequal variances by transforming all the data values
- Nonparametric tests: dealing with nonnormality by employing a test that does not make the normality assumption of the one-way blocked analysis of variance

Transformations:- Transformations (a single function applied to each data value) are applied to correct problems of nonnormality or unequal variances. For example, taking logarithms of sample values can reduce skewness to the right. Transforming all the samples to remedy nonnormality often results in correcting heteroscedasticity (unequal variances), and in eliminating interactions (i.e., creating a simple additive model for blocks and treatments); however, a transformation that corrects nonnormality may create interactions where none existed before. The same transformation should be applied to all samples. Unless scientific theory suggests a specific transformation
a priori, transformations are usually chosen from the "power family" of transformations, where each value is replaced byx**p, wherepis an integer or half-integer, usually one of:

- -2 (reciprocal square)
- -1 (reciprocal)
- -0.5 (reciprocal square root)
- 0 (log transformation)
- 0.5 (square root)
- 1 (leaving the data untransformed)
- 2 (square)

For p = -0.5 (reciprocal square root), 0, or 0.5 (square root), the data values must all be positive. To use these transformations when there are negative and positive values, a constant can be added to all the data values such that the smallest is greater than 0 (say, such that the smallest value is 1). (If all the data values are negative, the data can instead be multiplied by -1, but note that in this situation, data suggesting skewness to the right would now become data suggesting skewness to the left.) To preserve the order of the original data in the transformed data, if the value of p is negative, the transformed data are multiplied by -1.0; e.g., for p = -1, the data are transformed as x --> -1.0/x. Taking logs or square roots tends to "pull in" values greater than 1 relative to values less than 1, which is useful in correcting skewness to the right. Transformation involves changing the metric in which the data are analyzed, which may make interpretation of the results difficult if the transformation is complicated. If you are unfamiliar with transformations, you may wish to consult a statistician before proceeding.

Nonparametric tests:- Nonparametric tests are tests that do not make the usual distributional assumptions of the normal-theory-based tests. For the one-way blocked ANOVA, the most common nonparametric alternative tests are Friedman's test and the Quade test. Although Friedman's test does not assume normality of the distributions for the sample populations, it does assume that the populations have the same distribution, except for a possible difference in the population medians. Thus Friedman's test will not address the problem of inequality of variances. Also, as with the one-way blocked ANOVA, it is assumed that the measurement errors are identically distributed and independent of each other, and that there is no interaction between blocks and treatments.
If the sampled values do indeed come from populations with normal distributions, then the one-way blocked ANOVA is the most powerful test of the equality of the means, meaning that no other test is more likely to detect an actual difference among the means. (If a distribution is symmetric, its mean and median are both equal to the center of symmetry. Since the normal distribution is symmetric, the one-way blocked ANOVA can also be viewed as testing for differences among the sample medians, if the normality assumption holds.) If the population distributions are not normal, however, Friedman's test may be more powerful at detecting differences between the sample medians.

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