Examples Of Descriptive Statistics

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Introduction
Statistics is the practice or science of collecting and analyzing numerical data in large quantities, especially for the purpose of inferring proportions in a whole from those in a representative sample. The following paper seeks to provide basic knowledge in some specific areas of statistical data analysis.
Descriptive statistics
Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way. Descriptive statistics do not allow us to make conclusions beyond the data we have analyzed or reach conclusions regarding any hypotheses we might have made. They are just simply a way to describe our data. When data is presented to its users, it can be hard to understand
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In this case, the frequency distribution is simply the distribution and pattern of marks scored by the 100 students from the lowest to the highest. We can describe this central position using a number of statistics, including the mode, median, and mean.
Measures of dispersion: Measures of dispersion are ways of summarizing a group of data by describing how spread out the scores are. For example, the mean score of our 100 students may be 65 out of 100. However, not all students will have scored 65 marks. The scores will be spread out. Some will be lower and others higher. Measures of spread help us to summarize how spread out these scores are. Range, standard deviation, quartiles, absolute deviation and variance are some of the measures of dispersion.
Inferential
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There are several different types of statistical testing to choose from depending on whether the data follows normal distribution and the aim of the study. You have to define the level of measurement of each variable to be included in the analysis. Usually data is in one of the following four categories: Nominal data, Ordinal data, Interval data and Ratio data. Aim of the study is another pre-requisite that must be clearly defined for the selection of statistical test (Jaykaran). Next, is to select the correct statistical analysis, you have to clarify what you want to find out. The research question or hypothesis is typically phrased in terms of finding differences, relationships, or predicting. For relationship questions with interval, ordinal-level, or ratio-level variables, the correct statistical analysis is typically Spearman or Pearson correlations. Lastly, the sample size calculation or power analysis is directly related to the statistical test that is chosen. The sample size calculation is based on the power, the effect size, and the

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