Descriptive and Inferential Statistics (Category:Concept, Level:Basic)

Descriptive statistics organize, describe, and summarize data using numbers and graphical techniques. This branch of statistics uses a set of standard measures such as percent, averages, and variability, as well as simple graphs, charts, and tables. Descriptive statistics help you to better understand your data by describing and summarizing its basic features. You learn how to generate and understand numerical summaries. These include frequency; measures of location, including minimum, maximum, percentiles, quartiles, and central tendency (mean, median, and mode); and measures of dispersion or variability, including range, interquartile range, variance, and standard deviation. The graphical summaries you learn include the histogram, normal probability plot, and box plot. The goals when you're describing data are to:

• screen for unusual data values,
• inspect the spread and shape of your data,
• characterize the central tendency, and
• draw preliminary conclusions about your data.

Is the data as error free as possible? What unique features can you identify? Are there data values that cluster or show some unusual shape? Does your data include any possible outliers? When you have a basic understanding of your data, then you can use inferential statistics.

Inferential statistics is the branch of statistics concerned with drawing conclusions about a population from analysis of a random sample drawn from that population. It is also concerned with the precision and reliability of those inferences. Inferential statistics generalize from data you observe to the population that you have not observed. Descriptive statistics describe your sample data, but inferential statistics help you draw conclusions about the entire population of data. Descriptive statistics can also be referred to as exploratory data analysis, or EDA. Inferential statistics can also be called explanatory modeling.