Like a data set is always specific to some particular group, descriptive statistics are also bound to such particular team. This means that descriptive statistics can always state something about such group, individually of whether the data is usable further or not.
That is, these statistics usually describe properties of the particular group under study (eg, of a sample). They in many cases are used for assessing the quality of the data within the data set and, therefore, for ascertaining if it is appropriate to carry on further into utilizing inferential statistics 2.
Statistical procedures could be divided into two main categories: inferential statistics and descriptive statistics.
Before discussing the differences between inferential and descriptive statistics, we should first be common with two essential concepts in social science statistics: sample and population. A sample is actually a relatively tiny subset of objects, people, events or groups that is chosen from the population.
A population is the overall set of groups, individuals, events or objects that the researcher is researching.
Descriptive statistics contains statistical procedures that we use to explain the population we are researching. The data could be collected through either a population or a sample, but the outcomes help us explain and organize data. Descriptive statistics can simply be used to explain the group that is getting studying.
Inferential statistics is focused on making predictions or inferences of a population through observations and analyses of a sample. That is, we can take the outcomes of an analysis utilizing a sample and can generalize it to the bigger population that the sample symbolizes.
Statistics are utilized by researchers to explain data and interactions among data. For example, if one asks the way a class of students did on an exam, it would be inefficient to communicate every single score. Rather, a more efficient approach is to provide the average to provide a general indication of the way the students performed on the exam.
Descriptive statistics are employed to describe data in a concise, clear way. Descriptive statistics are overview indicators of bigger groups of data. The example above shows how descriptive statistics can be utilized to reduce large sums of information into a few summary indicators thus decreasing class scores to some class average. Two essential summary techniques for data are measures of central tendency (average or typical scores) and measures of dispersion (variability or spread of scores).
1. Measures of Variability
2. Measures of Central Tendency
3. Measure of Relative Position
4. Graphing Data