The two main divisions of statistics are descriptive statistics and inferential statistics. Although they are both unique in their own right, they are normally used in connection with one another as part of a wider statistical analysis of a given set of data. Descriptive statistics are those which help reveal patterns in one or more sets of data through numerical analysis, whilst inferential statistics refer to those used after analysis to make conclusions, evaluations and further predictions based on previous research.
To further illustrate the idea of descriptive and inferential statistics as unique fields of study, we can look at them both individually. Whilst we do so, we should continue to remind ourselves that both sub-divisions are used in coherence with one another, with one following on from the other (inferential is used after descriptive).
Descriptive statistics simply describe data - hence the rather self-explanatory title they are given. They include modes, means, ranges and frequencies which help demonstrate striking trends, similarities and differences within a set of data or between multiple sets of data. As we progress down the line, we can start to manipulate the data we have to make further predictions and provide additional commentary. This is precisely where inferential statistics come in to the equation, as you will see shortly.
Inferential statistics require human inference - again, as the name would suggest. We can look at pieces of descriptive data which show certain trends to draw conclusions based on the patterns displayed. This can lead to further sampling, matching and experimentation to see if a set of data links in with another prediction based on original descriptive data. Without prior data analysis of a given set of statistical information, however, it would be practically impossible to use inference techniques. Yet again, this reiterates the importance of both branches of statistics being used in connection with one another.
To further illustrate the idea of descriptive and inferential statistics as unique fields of study, we can look at them both individually. Whilst we do so, we should continue to remind ourselves that both sub-divisions are used in coherence with one another, with one following on from the other (inferential is used after descriptive).
Descriptive statistics simply describe data - hence the rather self-explanatory title they are given. They include modes, means, ranges and frequencies which help demonstrate striking trends, similarities and differences within a set of data or between multiple sets of data. As we progress down the line, we can start to manipulate the data we have to make further predictions and provide additional commentary. This is precisely where inferential statistics come in to the equation, as you will see shortly.
Inferential statistics require human inference - again, as the name would suggest. We can look at pieces of descriptive data which show certain trends to draw conclusions based on the patterns displayed. This can lead to further sampling, matching and experimentation to see if a set of data links in with another prediction based on original descriptive data. Without prior data analysis of a given set of statistical information, however, it would be practically impossible to use inference techniques. Yet again, this reiterates the importance of both branches of statistics being used in connection with one another.
