DESCRIPTIVE AND INFERENTIAL STATISTICS

DESCRIPTIVE AND INFERENTIAL STATISTICS

1. DESCRIPTIVE STATISTICS

   • Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data.
   • Descriptive statistics are very important because if we simply presented our raw data it would be hard to visulize what the data was showing, especially if there was a lot of it.
     • therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data.
   • Typically, there are two general types of statistic that are used to describe data:
     • Measures of central tendency:
       • these are ways of describing the central position of a frequency distribution for a group of data.
     • Measures of spread:
       • these are ways of summarizing a group of data by describing how spread out the scores are.
   • When we use descriptive statistics it is useful to summarize our group of data using a combination of tabulated description (i.e., tables), graphical description (i.e., graphs and charts) and statistical commentary (i.e., a discussion of the results).

2. INFERENTIAL STATISTICS

   • Inferential statistics are techniques that allow us to use these samples to make generalizations about the populations from which the samples were drawn.
     • It is, therefore, important that the sample accurately represents the population.
       • The process of achieving this is called sampling
   • Inferential statistics arise out of the fact that sampling naturally incurs sampling error and thus a sample is not expected to perfectly represent the population.
   • The methods of inferential statistics are:
     • the estimation of parameter(s)
     • testing of statistical hypotheses.