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Chapter 7

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PSYC 1010
Anne Bergen

Chapter 7 Hypothesis Testing With z Tests  A z-test is a hypothesis test in which we compare data from one sample to a population for which we know the mean and the standard The z Table  Hypothesis testing allows us to draw conclusions about the data we have collected to test a hypothesis  There are three different ways to identify the same point beneath the normal curve with the z rest o Raw score o Z score o Percentile ranking  The z table is a tool that allows us to transition from one to another of these ways to identify a point, and it is a fundamental to of hypothesis testing Raw Scores, z Scores, and Percentages  The z table allows us to translate the standardized z distribution into percentages and individual z scores in percentile ranks  We can determine the percentage associated with a given z statistic by following 2 steps o Convert raw scores into z scores o Look up a given z score on the z table to find the percentage of scores between the mean and that z score  The z scores on the table don't have negatives o So you just subtract 50 for the percentage of the z table corresponding to your z score  Or add it depending on what you want to find and the numbers The Z table and Distributions of Means  Using the z table to determine percentages and z statistics for distributions of means is identical as for the distribution of scores o Except there is one extra step of fist having to calculate the means and standard error for the distribution of means The Assumptions and the Steps of hypothesis Testing The Three Assumptions for Conducting Analyses  An assumption is a characteristic that we ideally require the population from which we are sampling to have so that we can make accurate inferences  A parametric test is an inferential statistical analysis based on a set of assumptions about the population  A nonparametric test is an inferential statistical analysis that is not based on a set of assumptions about the population  Before outlining the steps of hypothesis testing o First we assume that the dependent variable is assessed using a scale measure o Second we assume that th
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