In order to use any statistical package (such as SPSS, Minitab, SAS, etc.) successfully, there are some common statistical terms that you should know. This document introduces the most commonly used statistical terms. These terms serve as a useful conceptual interface between methodology and any statistical data analysis technique. Irrespective of the statistical package that you are using, it is important that you understand the meaning of the following terms.
Most statistical data analysis involves the investigation of some supposed relationship among variables. A variable is therefore a feature or characteristic of a person, a place, an object or a situation which the experimenter wants to investigate. A variable comprises different values or categories and there are different types of variables.
Quantitative variables are possessed in degree. Some common examples of these types of variables are height, weight and temperature.
Qualitative variables are possessed in kind. Some common examples of these types of variables are sex, blood group, and nationality.
Often, most statistical data analysis wants to test some sort of hypothesis. A hypothesis is therefore a provisional supposition among variables. It may be hypothesized, for example, that tall mothers give birth to tall children. The investigator will have to collect data to test the hypothesis. The collected data can confirmed or disproved the hypothesis.
The independent variable has a causal effect upon another, the dependent variable. In the example hypothesized above, the height of mothers is the independent variable while the height of children is the dependent variable. This is so because children heights are suppose to depend on the heights of their mothers.
There are basically three kinds of data:
These are data taken from an independent scale with units. Examples include height, weight and temperature.
These are data collected from ranking variables on a given scale. For example, you may ask respondents to rank some variable based on their perceived level of importance of the variables.
Merely statements of qualitative category of membership. Example include sex (male or female), race (black or white), nationality (British, American, African, etc.).
It should be appreciated that both Interval and Ordinal data relate to quantitative variables while Nominal data refers to qualitative variables.
The availability of powerful statistical packages such as SPSS, Minitab, and SAS has made statistical data analysis very simple. It is easy and straightforward to subject a data set to all manner of statistical analysis and tests of significance. It is, however, not advisable to proceed to formal statistical analysis without first exploring your data for transcription errors and the presence of outliers (extreme values). The importance of thorough preliminary examination of your data set before formal statistical analysis can not be overemphasized.
Know exactly how you are going to analyse your data before you even begin to think of how to collect it. Ignoring this advice could lead to difficulties in your project.