# Standardized Testing and Personality Assessments

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Chapter 12 introduces the reader to the true definition of statistics, without scaring them half to death. The book breaks statistics down in two parts: descriptive and inferential. The type that is dealt with in this chapter is descriptive statistics. The simple definition of descriptive statistics are that they are just numbers in different forms, for example, percentages, numerals, fractions, and decimals. The book gives an example of a grade point average being a descriptive statistic. It is becoming increasingly important for classroom teachers to be able to understand and interpret statistics because of increasing calls for acountablity. Being able understand various types of statistics, there uses and limitations, will put the educator who does at an advantage. Instead of just averaging grades, there are important questions that every educator should want to know the answers to regarding their classroom. Questions like, how many people are above average and how many scored above the cutoff passing score, are questions that can't be answered without some working knowledge of statistics. The bar graph explanation was pretty clear in the chapter, but that might be just because it's the most frequently used graph to convey statistical data. Everyone is familiar with the bar graph, but when it comes down to frequency polygons things get a little fuzzy. Chapter 13 explains how distributions can have the same values for the mode, median, and mean but are different in the way the scores are spread out. The variability estimate helps determine how compressed or expanded the distributions are. The range is the easiest way to estimate variability and its determined from subtracting the lowest score from the highest score. In the case of the range, things can get thrown off if an extreme score is present. One way of preventing this from happening is to use the semi-interquartile range. This score is determined by taking the middle 50% of the scores in a distribution. The upper 25% and the lower 25% are not entered into its final computation. Standard deviation is an estimate of variability that accompanies the mean in describing a distribution. You are taking a look at each distribution to see how far away each score deviates from the mean. The normal distribution is a type of symmetrical distribution that is mathematically determined and has fixed properties. It is basically used as a model to base statistical decisions. These are methods that can most definitely be used in the classroom when analyzing data.