![]() The second class, indicators, are used to explain our outcomes. Therefore we need a second class of variables. For example, explaining school_rating as a result of state_percentile_16 (test scores) is circular logic. Some of these measurements, such as state_percentile_16, avg_score_16 and school_rating, are outcomes these outcomes cannot be used to explain one another. ![]() All data in the output above are measurements. Measurements are variables that can be quantified. Looking at the output above, Sally's variables can be put into two classes: measurements and indicators. Each row header represents a descriptive statistic about the corresponding column. The column headers in bold text represent the variables Sally will be exploring. To begin learning about the sample, Sally uses pandas' describe method, as seen below. For Sally, this involves developing a hypothesis about her sample of middle Tennessee schools and applying it to her population of all schools in Tennessee.įor now, Sally puts aside inferential statistics and digs into descriptive statistics. Inferential statistics allow us to make hypotheses (or inferences) about a sample that can be applied to the population. A small standard deviation indicates the data are close to the mean, while a large standard deviation indicates that the data are more spread out from the mean. Deviation is most commonly measured with the standard deviation. Central tendency refers to the central position of the data (mean, median, mode) while the deviation describes how far spread out the data are from the mean. Within descriptive statistics, there are two measures used to describe the data: central tendencyand deviation. Sally opens her stats textbook and finds that there are two major types of statistics, descriptive and inferential.ĭescriptive statistics identify patterns in the data, but they don't allow for making hypotheses about the data.
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