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Descriptive statistics summarize the characteristics of a data set. There are three types: distribution, central tendency, and variability.
Descriptive statistics is a branch of statistics that deals with the summarization and description of collected data. This type of statistics is used to simplify and present data in a manner that is easy to understand, often through visual or numerical methods.
Simply put, descriptive statistics describe and summarise the sample itself, while inferential statistics use the data from a sample to make inferences or predictions about a population.
Descriptive statistics, such as mean, median, and range, help characterize a particular data set by summarizing it. It also organizes and presents that data in a way that allows you to interpret it.
Whether the researcher’s goal is to describe trends in populations, create new measures of key phe- nomena, or simply describe methods used to identify causal effects, descriptive analysis is a valuable research tool (see Box 5).
Descriptive statistics is the analysis and summarization of data to gain insights into its characteristics and distribution [1]. Descriptive statistics help researchers generate study ideas and guide further analysis by allowing them to explore data patterns and trends [2].
What are statistics and why do we need them? This chapter introduces descriptive statistics and then creates a bridge from describing data concisely to answering questions using hypothesis testing and inferential statistics.
Moreover, descriptive statistics serve as a vital starting point in the research process, enabling academics to identify trends, anomalies, and patterns that might require further investigation through inferential statistics or other advanced analytical techniques.
In this section, we focus on presenting descriptive statistical results in writing, in graphs, and in tables—following American Psychological Association (APA) guidelines for written research reports.
Introduction to Descriptive Statistics. 17.871 Spring 2015. Reasons for paying attention to data description. Double-check data acquisition. Data exploration. Data explanation. Key measures. Describing data. Key distinction. Population vs. Sample Notation. Mean. i n x. X n. Variance, Standard Deviation of a Population. . n ( x. ) 2.