Читать книгу Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences - Paul J. Mitchell - Страница 33

In the majority of our experiments, the data we obtain will be numerical in nature. However, it is important to carefully distinguish the nature of the data being analysed since not all data may be treated similarly. Consequently, the type of statistical analysis we employ depends on the type of data obtained; i.e. statistical tests are generally specific for the kind of data we wish to analyse.

In general terms, there are three kinds of data, although as can be seen below, there may be further differences within measurement data depending on form and scale.

1 Nominal (categorical) dataSuch data are where either numerals are applied to attributes or categories that are not strictly measures but allow accurate identification, or where the number of observations in a category may be recorded. For example, hair colour may be a category and the frequency of individuals with black, brown, red, blonde, or brunette hair is recorded. The results of survey data are typically categorical.

2 Ordinal dataSuch data are where a scale with ranks is employed to order the observations. The rationale behind the ranks is that the values may be ranked in order (which makes it an ordinal scale) of magnitude. Data obtained from well‐being scales are examples of ordinal (ranked) data.

3 Measurement dataNumerical data may exist in two forms and in three types of scale.Form of measurement data

1 Discrete data (aka meristic) are generally counts and may only be discrete values normally represented by integers.

2 In contrast, continuous data are those observations or measurements where the precision is only limited by the experimenter and the equipment used.

Types of scale

1 An interval scale is where the values are measured on a scale where the differences are uniform but ratios not so. For example, on the Celsius temperature scale, the difference between 5° and 10° is the same as between 10° and 15°, but the ratio between 5° and 15° does not imply that the latter is three times as warm as the former.

2 A ratio scale is where the values have a meaningful zero point. Examples here include length, weight, and volume. Thus, 15 cm is three times longer than 5 cm, 2 kg is twice as heavy as 1 kg, and 200 ml is four times the volume of 50 ml.

3 A circular scale may be used when one measures annual dates, clock times, etc. Generally, neither differences nor ratios of data obtained from circular scales are sensible or useful derivatives, and consequently special methods are employed for such data; such methods are outside the scope of this book.

A further issue we need to consider before deciding which statistical methods are appropriate to apply to our experimental data concerns the distribution of our data sets.