Hypothesis Testing Psyc-21621
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This 3 page Class Notes was uploaded by Amy Turk on Wednesday April 27, 2016. The Class Notes belongs to Psyc-21621 at Kent State University taught by Dr. Gordon in Spring 2016. Since its upload, it has received 3 views. For similar materials see Quantitative Methods Psych I in Psychlogy at Kent State University.
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Date Created: 04/27/16
Chapter 8 - Hypothesis Testing ● Step 1: Restate the research question as a hypothesis with a null hypothesis about the populations ● make a claim about the population ○ the null hypothesis ○ H0 ● determine a relevant alternative claim ○ the alternative hypothesis ○ H1 ● Step 2: determine characteristics of the comparison distribution ○ represents the population situation if the null hypothesis is true ○ it is the distribution you are comparing your sample to ● Step 3: determine the cutoff sample score on the distribution at which the null hypothesis should be rejected ○ aka Critical Value or significance level = point at which the sample differs from the comparison distribution ○ have to use z-scores, probabilities, and percents ● you want to reject the null in favor of the alternative hypothesis ○ the 5% level ■ the result is in the outer 5% of the distribution ■ means the result happened by chance less than 5% of the time ■ if you are doing a one-tailed test, look up in Table B-1 ● your critical Z is 1.64 (.05 in the tail) ■ for a two-tailed test ● your critical Z is +/- 1.96 (.025 in each tail) ● Step 4: collect your data and determine the collected sample’s score on the comparison distribution ○ once we collect our data, we can calculate a z-score ● we can determine the specific probability of an outcome relative to the null hypothesis ● the z test = use a z-score to make a decision ● Step 5: decide whether to reject the null hypothesis ○ you compare your result from Step 4 to the cutoff z-score from step 3 ● if your outcome is unlikely to have happened by chance, i.e., probability of it being by chance is .05 or less… ○ reject the null hypothesis ● if your outcome was likely due to chance (p is greater than .05) ○ fail to reject the null hypothesis ○ you do not accept the null hypothesis The Hypothesis Testing Process 1. restate the question as a research question with a null hypothesis about the populations 2. determine characteristics of the comparison distribution 3. determine the cutoff sample score on the comparison distribution at which the null hypothesis should be rejected 4. determine your sample’s score on the comparison distribution 5. decide whether to reject/fail to reject the null hypothesis We are trying to decide whether to reject the null hypothesis ● which is another way of saying: is there an effect? What We Do With Alpha ● We are setting a boundary at which we can say “We are reasonably sure there is a relationship, and that the null hypothesis is false” ● We want to be sure our sample mean is noticeably different from the population ○ thus, if we randomly grabbed a sample from the population, we want the probability of obtaining our specific sample mean to be low ● determining the probability that the observed results could have occurred by chance alone (null hypothesis) ○ alternative hypothesis = did not happen by chance; there is some effect/difference ● want this to be a small probability (usually less than .05), because this means that the probability that the results we obtained happened by chance and chance alone is less than .05 ● stated another way… we randomly get these results by chance less than 5% of the time Statistical Significance ● states whether our result, based on our statistic, is significant ● tells us whether we have sufficient justification to reject the null hypothesis ● looking at meaningful patterns and determining whether or not it occurred by chance ● hypothesis = prediction about an event ● a hypothesis test = statistical method that uses sample data to evaluate a hypothesis about a population ● a hypothesis = a specific prediction about something; tied to a theory ● a bad hypothesis is vague ● hypotheses are always stated in terms of parameters and always use mathematical notation ● researcher considers the probability that the experimental procedure had no effect and that an observed result could have occurred by chance alone The Null Hypothesis ● original claim we make about the population ● usually has no effect or difference ● this is the hypothesis that we are looking for evidence against ● we want to reject the null Alternative Hypothesis ● there is a change, a difference, or a relationship for the general population ● two types… ○ two-sided = does not predict a direction of effect ○ one-sided = does predict a direction of effect ● one-sided = making a prediction about the direction of effect ○ greater than/less than ○ better/worse ● two-sided = no prediction about direction of effect ○ there’s a difference, but I’m not saying in what way
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