statistical analysis

(noun)

The process of examining data to draw conclusions or insights, and determine cause-and-effect patterns between events; for example determining the safety and efficacy of new drugs by drawing out a probability as to whether the fact that a patient got better (or worse) was due to the drug or some other (perhaps random) factor.

Related Terms

Examples of statistical analysis in the following topics:

  • Applications of Statistics

    • Statistics deals with all aspects of the collection, organization, analysis, interpretation, and presentation of data.
    • Statistics deals with all aspects of the collection, organization, analysis, interpretation, and presentation of data.
    • 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.
    • Statistical analysis of a data set often reveals that two variables of the population under consideration tend to vary together, as if they were connected.

  • Descriptive or Inferential Statistics?

    • Descriptive statistics and inferential statistics are both important components of statistics when learning about a population.
    • Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented.
    • These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation.
    • More recently, a collection of summary techniques has been formulated under the heading of exploratory data analysis: an example of such a technique is the box plot .
    • In the business world, descriptive statistics provide a useful summary of security returns when researchers perform empirical and analytical analysis, as they give a historical account of return behavior.

  • What Is Statistics?

    • Statistics is the study of the collection, organization, analysis, interpretation, and presentation of data.
    • Statistics is the study of the collection, organization, analysis, interpretation, and presentation of data.
    • A statistician is someone who is particularly well-versed in the ways of thinking necessary to successfully apply statistical analysis.
    • In this book, Al-Kindi provides a detailed description of how to use statistics and frequency analysis to decipher encrypted messages.
    • The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general.

  • References

    • An analysis of transformations, Journal of the Royal Statistical Society, Series B, 26, 211-252.
    • Applied Linear Statistical Models, McGraw-Hill/Irwin, Homewood, IL.

  • Analyzing Data

    • In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).
    • Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification.
    • Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data.
    • All are varieties of data analysis.
    • Statistical market research tools are used.

  • Statistical Graphics

    • Statistical graphics allow results to be displayed in some sort of pictorial form and include scatter plots, histograms, and box plots.
    • Statistical graphics are used to visualize quantitative data.
    • Whereas statistics and data analysis procedures generally yield their output in numeric or tabular form, graphical techniques allow such results to be displayed in some sort of pictorial form.
    • Exploratory data analysis (EDA) relies heavily on such techniques.
    • Statistical graphics developed through attention to four problems:

  • Exploratory Data Analysis (EDA)

    • Exploratory data analysis is an approach to analyzing data sets in order to summarize their main characteristics, often with visual methods.
    • Tukey's EDA was related to two other developments in statistical theory: robust statistics and nonparametric statistics.
    • Both of these try to reduce the sensitivity of statistical inferences to errors in formulating statistical models.
    • Exploratory data analysis, robust statistics, and nonparametric statistics facilitated statisticians' work on scientific and engineering problems.
    • Tukey held that too much emphasis in statistics was placed on statistical hypothesis testing (confirmatory data analysis) and more emphasis needed to be placed on using data to suggest hypotheses to test.

  • Communicating Statistics

    • Visual tools can be an effective way of incorporating statistics in your persuasive speech.
    • Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data.
    • Your audience is much more likely to believe you if you incorporate statistics.
    • Statistics can be difficult to understand on their own, though.
    • As a result, consider using visual tools such as tables, graphs, and maps to make statistics more understandable for your audience.

  • Introduction

    • Statistics is the study of how best to collect, analyze, and draw conclusions from data.
    • It is helpful to put statistics in the context of a general process of investigation:
    • Statistics as a subject focuses on making stages 2-4 objective, rigorous, and efficient.
    • That is, statistics has three primary components: How best can we collect data?
    • And what can we infer from the analysis?

  • Statistical Literacy

    • What type of analysis would be done for each type of design and how would the choice of designs affect power?