Answer:
Descriptive analysis
In the descriptive statistics The data could be collected from either a sample or a population, but
the results help us organize and describe data. Descriptive statistics can only
be used to describe the group that is being studying. That is, the results
cannot be generalized to any larger group.
Descriptive statistics are
useful and serviceable if you do not need to extend your results to any larger
group. However, much of social sciences tend to include studies that give us
“universal” truths about segments of the population, such as all parents, all
women, all victims, etc.
Descriptive analysis refers to transformation of raw
data into a form that will facilitate easy understanding and interpretation.
Descriptive analysis deals with summary measures relating to the sample data.
The common ways of summarizing data are by calculating average, range, standard
deviation, frequency and percentage distribution. Below is a set of typical
questions that are required to be answered under descriptive statistics:
- What is the average income of the sample?
- What is the standard deviation of ages in the sample?
- What percentage of sample respondents are married?
- What is the median age of the sample respondents?
- Which income group has the highest number of user of product in question in the sample?
- Is there any association between the frequency of purchase of product and income level of the consumers?
Inferential Statistics
Inferential statistics is
concerned with making predictions or inferences about a population from
observations and analyses of a sample. That is, we can take the results of an
analysis using a sample and can generalize it to the larger population that the
sample represents. In order to do this, however, it is imperative that the
sample is representative of the group to which it is being generalized.
Examples of inferential
statistics include linear regression analyses, logistic regression analyses,
ANOVA, correlation analyses, structural equation modeling, and survival
analysis, to name a few.
Under inferential statistics, inferences are drawn on
population parameters based on sample results. The researcher tries to
generalize the results to the population based on sample results. The analysis
is based on probability theory and a necessary condition for carrying out
inferential analysis is that the sample should be drawn at random.
Is the proportion of satisfied workers significantly more for skilled
workers than for unskilled works?