In orthopaedics, data are often not Normally distributed. Occasionally however, data that are not Normally distributed can be ** transformed** to make the distribution Normal. For example, by taking the logarithm or square our data. This process is called transformation of data. For example, the concentration of H

_{3}O

^{+}in the peripheral blood is not Normally distributed, but the pH (-Log [H

_{3}O

^{+}]) is.

Following transformation, it might be possible to use a more powerful parametric test (provided normality can be demonstrated). As stated, it is possible to use a non parametric test on parametric data. However, a parametric test can’t be used on non parametric data. If we are in doubt about the distribution of our data, it is always possible to use a non parametric test.

In analysing the outcome of hip replacements, functional scores (such as Harris hip score) are often used. They consist of a questionnaire that contains questions for pain, function and range of movement. A score is given for each category. They are added together and expressed as a percentage of normal function. At first glance, the functional score obtained appears to be continuous data. So, provided normality can be demonstrated it seems reasonable to use a parametric test (such as the t-test). However, when we look closer, the functional score is obtained by adding ** ordinal** data together (the answer to each question is given as a number, but in essence the data is ordinal). Hopefully it is obvious that by adding ordinal data together, the data remain ordinal and can never become continuous. Therefore, the data are not Normally distributed and it is wrong to use a parametric test. It also seems rather strange to add scores for function, pain and range of movement. These measures are completely different and should not be added together.