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Interential Statistics
* Statistics used to draw conclusions about your data and how they can be applied to other groups
* 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
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