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Chapter 8

Chapter 8.docx

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Department
Psychology
Course Code
PSYC 1010
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c

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th Chapter 8- Confidence Intervals, Effect Size, and Statistical Power Feb. 25 Confidence Intervals - An interval estimate, based on the sample statistic, that includes the population mean a certain percentage of the time- were we to sample from the same population repeatedly - Centered around the mean of the sample - Point Estimate- a summary statistic from a sample that is just one number used as an estimate of the population parameter - Interval Estimates- based on a sample statistic and provides a range of plausible values for the population parameter  We can use a sample to calculate a point estimate (one number such as mean, for our population) or to calculate an interval estimate (a range of plausible numbers, such as range of means, for our population) Calculating Confidence Intervals with z Distributions - Steps in calculating a confidence interval  Draw a picture of a distribution that will include the confidence interval - Draw a normal curve with the sample mean in the center  Indicate the bounds of the confidence interval on the drawing - Write the percentages under each segment of the curve  Determine the z statistics that fall at each line marking the middle 95% - For a 95% confidence interval it is always -1.96 and 1.96  Turn the z statistics back into raw means - Because we have a sample mean rather than a raw score, we use a distribution of means by calculating the standard error  Check that the confidence interval makes sense - The sample mean should fall exactly in the middle of the two ends of the interval Effect Size and Prep - Statistically significant means that those findings are unlikely to occur if in fact the null hypothesis is true - As sample size increases so does the test statistic (if all else stays the same) - Due to this- a small difference might not be statistically significant with a small sample, but might be statistically significant with a large sample - Effect size- indicates the size of a difference and is unaffected by a sample size  Tells us how much two populations do not overlap- the less overlap, the bigger the effect size  Amount of overlap between two di
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