descriptive statistics

(noun)

A branch of mathematics dealing with summarization and description of collections of data sets, including the concepts of arithmetic mean, median, and mode.

Related Terms

Examples of descriptive statistics in the following topics:

  • Descriptive or Inferential Statistics?

    • 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

    • Descriptive statistics are numbers that are used to summarize and describe data.
    • Descriptive statistics are just descriptive.
    • Here we focus on (mere) descriptive statistics.
    • Some descriptive statistics are shown in Table 1.
    • For more descriptive statistics, consider Table 2.

  • Distorting the Truth with Descriptive Statistics

    • 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:

  • Univariate descriptive statistics

    • As with any descriptive statistics, the scale of measurement (binary or valued) does matter in making proper choices about interpretation and application of many statistical tools.
    • The data that are analyzed with statistical tools when we are working with network data are the observations about relations among actors.
    • What we would like to summarize with our descriptive statistics are some characteristics of the distribution of these scores.
    • No statistics on distributional shape (skew or kurtosis) are provided by UCINET.
    • Univariate descriptive statistics for Knoke information and money whole networks

  • Lab: Descriptive Statistics

  • Applications of Statistics

    • This is called descriptive 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.

  • Descriptive and Correlational Statistics

    • Descriptive and correlational statistics help interpret the relationship, or relatedness, between observable variables.
    • Descriptive and correlational statistics are used in psychology to describe data and illustrate the results.
    • As the name suggests, descriptive statistics describe how the data looks.
    • These statistics are not used to make any inferences from the collected data in relation to any population outside of the sample group.
    • Tools used for descriptive statistics include quantitative measures such as the mean, median, and mode, as well as a distribution curve.

  • Introduction to describing one network

    • Most social scientists have a reasonable working knowledge of basic univariate and bivariate descriptive and inferential statistics.
    • The application of statistics to social networks is also about describing distributions and relations among distributions.
    • Most of the descriptive statistical tools are the same for attribute analysis and for relational analysis -- but the subject matter is quite different!
    • Instead, alternative numerical approaches to estimating standard errors for network statistics are used.
    • So, let's begin with the simplest univariate descriptive and inferential statistics, and then move on to somewhat more complicated problems.

  • What Is Statistics?

    • In short, statistics is the study of data.
    • 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 probability-based 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, Al-Kindi provides a detailed description of how to use statistics and frequency analysis to decipher encrypted messages.

  • Statistical Literacy

    • The experiment is described here: http://www.sciencenews.org/view/generic/id/333911/description/Saffron_takes_on_cancer.
    • What method could be used to test whether this difference between the experimental and control groups is statistically significant?