Examples of descriptive statistics in the following topics:

 Descriptive statistics and inferential statistics are both important components of statistics when learning about a population.
 Descriptive statistics is the discipline of quantitatively describing the main features of a collection of data, or the quantitative description itself.
 Descriptive statistics are distinguished from inferential statistics in that descriptive statistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent.
 This generally means that descriptive statistics, unlike inferential statistics, are not developed on the basis of probability theory.
 Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented.

 Descriptive statistics are numbers that are used to summarize and describe data.
 Any other number we choose to compute also counts as a descriptive statistic for the data from which the statistic is computed.
 Several descriptive statistics are often used at one time to give a full picture of the data.
 You probably know that descriptive statistics are central to the world of sports.
 There are many descriptive statistics that we can compute from the data in the table.

 Descriptive statistics can be manipulated in many ways that can be misleading, including the changing of scale and statistical bias.
 Descriptive statistics can be manipulated in many ways that can be misleading.
 Bias is another common distortion in the field of descriptive statistics.
 Descriptive statistics is a powerful form of research because it collects and summarizes vast amounts of data and information in a manageable and organized manner.
 To illustrate you can use descriptive statistics to calculate a raw GPA score, but a raw GPA does not reflect:


 Statistical models can also be used to draw statistical inferences about the process or population under study—a practice called inferential statistics.
 Descriptive statistics and analysis of the new data tend to provide more information as to the truth of the proposition.
 This data can then be subjected to statistical analysis, serving two related purposes: description and inference.
 Descriptive statistics summarize the population data by describing what was observed in the sample numerically or graphically.
 In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount as simply as possible.


 As one would expect, statistics is largely grounded in mathematics, and the study of statistics has lent itself to many major concepts in mathematics, such as:
 It includes descriptive statistics (the study of methods and tools for collecting data, and mathematical models to describe and interpret data) and inferential statistics (the systems and techniques for making probabilitybased decisions and accurate predictions based on incomplete data).
 Statistics itself also provides tools for predicting and forecasting the use of data and statistical models.
 Statistical methods date back at least to the 5th century BC.
 In this book, AlKindi provides a detailed description of how to use statistics and frequency analysis to decipher encrypted messages.


 What method could be used to test whether this difference between the experimental and control groups is statistically significant?

 Perhaps the fullest description was presented on the CNNMoney website (A service of CNN, Fortune, and Money) in an article entitled "Survey: iPhone retention 94% vs.