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Descriptive and inferential statistics are the standard topics of discussion in the field of statistics. In this article, we shall discuss the importance of these types of statistics. Interestingly, even though the statistical measures may be similar, the methodologies and the goals are entirely different.
Descriptive Statistics
By using descriptive statistics to graph and summarize a chosen group of data, it allows you to have an in-depth understanding of specific sets of observations. Descriptive statistics always describes a sample. This is straightforward since you choose an exciting group of data, record it, summarize it and eventually graph it to represent the group properties. Successfully, by using descriptive statistics, there are no any experiences of uncertainty since you will only be describing items that are measured. Here, you will not be trying to infer any properties about a large number of populations.
What are the Common Tools of Descriptive Statistics?
The following are tools for descriptive statistics:
Central Tendency
Statisticians use the median and the mean to locate the central part of a dataset. Measures of central tendency help the statisticians to explain where most values fall.
Dispersion
A measure of dispersion describes how far the data extends from the center. Statisticians use the standard deviation and the range to measure dispersion. If there is a low dispersion, it indicates that there is a tight cluster of values around the center. For higher dispersion, it shows that the values fall relatively far from the center. With dispersion, you can graph the frequency distribution.
Skewness
The measure of skewness helps the statisticians to have an idea whether the value distribution is skewed or symmetric.
Standard descriptive statistics can be presented by the use of both graphs and numbers. However, there are some descriptive analyses that one can perform such as using correlation and scatterplots to assess the relationships of paired data.
Inferential Statistics
This is a type of statistic that uses a sample data to make an inference of the entire population of the sample data. The ultimate goal for any inferential statistics is to conclude the whole population by use of a sample data. It is essential to have confidence that the selected sample accurately reflects the entire population. Hence, it is critical to define the study population, draw a sample from that population and use the analysis that will incorporate the sampling error.
Here, you do not have to pick a convenient group. However, by random sampling, it gives us the confidence that the picked sample effectively represent the entire population. However, the process of gathering this genuinely random sample can be complicated.
Statistical Tools for Analyzing Inferential Statistics
Hypothesis test, regression analysis, and confidence intervals are the commonly used methods of analyzing inferential statistics. Interestingly, the aforementioned inferential tools may produce quite similar descriptive values such as standard deviation and mean. However, they are used differently during the inference-making process.
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