Edge(X) Notes: Module 1 Variability Error Variability: - The measurement process may be prone to error, giving measurement variability. However, measurements can be made more accurate in a systematic way, such as by giving protocol for how a forearm is to be measured. - It is difficult to distinguish between measurement and natural variability in many sets of data, hence collectively these factors are referred to as the error variability. - Due to this error variability it is necessary to replicate our experiments, having multiple observations gives more accurate information about the nature and magnitude of the variability present. Group Variability: - In an example were male and female forearms are measured we also find differences in our observations because the two groups, males and females, do tend to have different lengths. This is also a form of variability. - However, despite the results we cannot conclusively claim, "males have longer forearm's than females" because this statement is not universally true - Instead we talk in averages, so we might claim more correctly that "the average forearm length of males is longer than the average forearm length of females". Sampling Variability: - Using the above example again, there is still more variability. The average lengths of forearm's calculated depends on the sample. This is referred to as sampling variability. - Most research articles in the biological sciences, particularly in medical and other human-related settings, are full of statistical statements and conclusions. Quantitative Variables: - Quantitative variables represent measurements, such as the height of a person or the temperature of an environment. - These are quite often continuous, taking any value over some range. Continuous variables capture the idea that measurements can always be made more precisely. - Discrete variables have only a small number of possibilities, such as a count of some outcomes or an age measured in whole years
Categorical Variables: - In statistics, a categorical variable is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category - Variables like sex are called nominal because they are arbitrary categories with no order between them. - Ordinal variables are those whose categories do have an order. A common example of this is in recording the age group someone falls into. We can put these groups in order because we can put ages in order. Qualitative data: - The term qualitative data is used to describe the type of data that comes from investigations that examine people's opinions, behaviours and experiences, usually captured through written answers to surveys, transcripts of interviews or field-based observations. Summary: In summary, types of data can be rapidly classified in a nested list: · Qualitative data: described by a characteristic · Categorical (i.e. described as a category) · Nominal data: an unordered list of categories · Quantitative data: described by a numerical scale · Numerical (i.e. described as a number) · Ordinal (in