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Psy/300 Week 4 Statistics

Decent Essays

When we look around us, we may not recognize that statistics is all around. Before I began to take this course “Statistics for Managers” I was not aware of how statistics actually worked. The first idea that came to my mind about statistics was probability. Not knowing statistics and probability are related because they both determine a possible outcome. Throughout this course I have learned what statistics is and how it works. In this paper, I will describe descriptive and inferential statistics, hypothesis developing and testing, the selection of statistical tests, and how to evaluate statistical results in analyzing data. Let’s say we are interested in measuring depression in women after having a child. There are 11 study participants …show more content…

According to the textbook, “Statistics for managers” Analysis of variance allows for one test to make comparisons between any numbers of groups so that there is just one probability for alpha error. Tanner, D. E., & Youssef–Morgan, C. M. (2013). However, ANOVA allows one to determine whether the differences between the samples are simply due to sampling error or whether there are effects that causes the mean in one group to differ from the mean in another. Often times, ANOVA is used to compare the equality of three or more means. For example, we will use the ANOVA to determine do TABE scores differ for low, middle, and high-income children? The effect size is also a part of ANOVA. The effect size is the main finding of a quantitative study. While a P value can inform the reader whether an effect exists, the P value will not reveal the size of the effect. Sullivan, Feinn, G. …show more content…

The chi-square tests were developed by Karl Pearson— the "Pearson Correlation" There are two chi-square procedures discussed in this class. Both of them have two names. The first is called the goodness-of-fit chi-square test, or alternatively the 1 × k (said "one by kay") chi-square. Tanner, D. E., & Youssef–Morgan, C. M. (2013). A chi-square goodness of fit test allows us to test whether hypothesized proportions differ from the observed proportions for a categorical variable. In other words, the chi-square test tells us whether there is a large difference between expected and collected numbers. After testing to see if there is a large difference will tells us if there is something causing a significant change. If it is a large difference we will reject the null hypothesis. Moreover, if the scores are too close they are the same. To use the number we find, we refer to the degrees of freedom, usually labeled as df for short, and is defined for the chi-square as the number of categories minus 1. Due to the nature of the chi-square test, you will always use the number of categories minus 1 to find the degrees of freedom. The reason this is done is because there is an assumption that your sample data is biased, and this helps shift your scores to allow for

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