# skalnik/PSYC-2015

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 Interential Statistics * Statistics used to draw conclusions about your data and how they can be applied to other groups Definitions * Population: All members of some predefined groups * Sample: Some subset of the population * Statistic: a numerical index (e.g., mean) based on sample data * Parameter: a numerical index based on population data Inferential Statistics * Sometimes we can study all members of a population, especially when they are a small group * Other times, we can't and so we need to sample from the population to whom we wish to generalize * Basically, alllows you to say how well your data apply to the larger population of which you want to generalize to * It relies on sampling distrbutions for making probabilitic statements about the populations based on sample data Remember Hypothesis testing? * What is the Null Hypothesis? * No relationship * What is the Alternative Hypothesis? * There is a relationship * What are the two possible outcomes we can have? * We can reject null hypothesis * Fail to reject Hypothesis Testing as Distributions * The Null Hypothesis suggests that our two sets of data come from the same population distribution * The Alternative Distribution suggests that they come from different populations * If they come from different populations we should be able to see the difference Alpha Level * The Alpha level: The probability of obtaining your particular result if the null hypothesis is really true * If you reject the null at Alpha = .05, then it means you believe the probability is very low (5 out of 100) that your research outcome is due to chance. Why .05? * In reality, it is an arbitrary number that developed over the years * But it still makes sense * Remember the Normal Distrbution One-Tailed vs Two-Tailed tests * Sometimes we are just interested in one groul being different from another * Other times we are interested in one group being not only different, but better or worse than another group