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Stats unit 2
| Question | Answer |
|---|---|
| The number of standard deviations above or below the means in this special normal distribution is called | z score |
| Which of the below need to be known in order to standardize an individual score? | mean, standard deviation, and percentiles |
| What is the equation for finding z score? | z = x-mean/sd |
| What is the equation for finding the raw score? | x = Z x SD + mean |
| What percent of scores fall under a z score of 1.96? | 97.5% |
| positive skew | tail on right side is longer than left side and most of the data values are clustered toward the lower end |
| negative skew | tail on the left side is longer than the right side and most of the data values are clustered toward the higher side |
| Central Limit Theorem | If you take sufficiently large random samples from a population with any shape of distribution, the distribution of the sample means will be approximately normal (bell-shaped) |
| What percentage of scores fall below a z score of -1? | 16% |
| What percent of scores would fall between the z scores 0 and 1.65? | 45% |
| sampling space | set of all possible outcomes |
| outcomes | a single possible result of a probability event |
| success | the specific outcome you are wanting to achieve |
| Law of Large Numbers | as sample size increases, the sample mean will get closer to the true population mean |
| In the z distribution, what percentage of raw scores will fall above z = -2? | 98% |
| In the z distribution, what percentage of raw scores will fall below z = 1? | 84% |
| In the z distribution, what percentage of raw scores would fall between -2 and 2? | 96% |
| What percentage of raw scores are below z = 1.75? | 95.99% |
| null hypothesis + symbolic notation | statement that says there is no effect/difference in the population: H0: U1 = U2 |
| research hypothesis | statement that there is an effect/difference in the population: H1: U1 does not equal U2, U1 < U2 (left-tailed), U1 > U2 (right-tailed) |
| How to calculate standard error | SE = population SD/square root sample size (n) |
| type I error | reject the null hypothesis when it is actually true aka false positive |
| type II error | fail to reject the null hypothesis when it is actually false aka false negative |
| When do we reject and fail to reject the null hypothesis? | - when p < 0.05, reject and when p > 0.05, fail to reject - if z > 1.96 or beyond the critical value reject, and if z < 1.96 or not past the critical value then fail to reject the null hypothesis |
| statistical significance | a result is statistically significant when is it unlikely to be due to change based on the significance level |
| How to calculate z statistic | z = x - pop. mean/SD |
| confidence intervals | range of values that you believe contains the true population mean |
| lower bound | lower bound = x - (1.96 x standard error) |
| upper bound | upper bound = x + (1.96 x standard error) |
| effect size | how big the difference is b/w groups or how strong the effect is |
| Cohen's d | d = M1-M1/SD total |
| statisitcal power | power to detect a real effect when one truly exists |
| What is the most effective way to increase statistical power? | increase sample size |
| Single sample t-test allows us to estimate ____ ? | population standard deviation |
| How many t distributions are there? | an infinite number |
| Where does the (N-1) correction occur? | in the formula for corrected standard deviation, but the corrected standard error formula uses regular N |
| What are the critical values for a t-test determined by? | degrees of freedom |
| A single-sample t test allows us to test the difference between the means of: | sample and population |
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