Statistics 210 Exam 2
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kristine_7 on March 21, 2012
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94 terms
Terms | Definitions |
|---|---|
 probability | equal to the number of outcomes classified as A divided by the total number of possible outcomes |
| chance | probability is likelihood that result occurred by ______ |
| random sampling | each individual in the population has an equal chance of being selected |
| sampling with replacement | each individual in the sample is returned to the population before another sample is drawn, keeps probabilities constant |
| random sampling, sampling with replacement | Two important assumptions of probability |
| 68-95-99.7% | Normal distributions use _______ rule |
| standard normal table | Uses z scores to determine probability |
| low probability region | corresponds to a relatively small proportion of the distribution, usually 0.05 or less |
| high probability region | corresponds to a relatively large proportion of the distribution, usually 0.95 or more |
| 1.96 | critical point for high and low probability is a z score of +/- |
| statistic | describes a characteristic of the sample, such as the mean of the sample scores |
| parameter | describes a characteristic of the population, such as the mean of all population scores |
| sampling error | the difference between the sample statistic and the population parameter |
| inferential statistics | techniques used to make generalizations about the population from which the sample was drawn, to determine the likelihood that the sample result is due to chance/error |
| result is due to chance, result is real | two possible interpretations of result |
| hypothesis testing | method of using sample data to evaluate a claim about a population parameter, a method for making rational decisions about the reality of out sample results |
| rational | systematic and logical with a relatively high likelihood of being correct |
| hypothesis | statement about the population parameter |
| null hypothesis | there is no effect, no relationship, no difference, nothing real in the population, IV does not affect DV, parameter = 0 |
| alternative hypothesis | there is an effect, relationship, difference, something is happening, IV affects DV, parameter ≠ 0 |
| fail to reject null hypothesis | if p is greater than alpha then we |
| rho | used when testing correlation/relationship between two variables in a population |
| mu | used when testing difference between mean of 2 groups in population |
| state the null and alternative hypotheses | step 1 of hypothesis testing |
| words, parameter notation | state null and alternative hypotheses in _______ and in ______ |
| set alpha | step 2 of hypothesis testing |
| collect data, compute statistic | step 3 of hypothesis testing |
| determine probability | step 4 of hypothesis testing |
| make decision about hypothesis | step 5 of hypothesis testing |
| reject null | if probability less than alpha |
| fail to reject null | if probability is greater than alpha |
| statistically significant | findings are said to be _______ when there is a low probability of the results occurring by chance alone, , does not indicate anything about magnitude of effect or relationship |
| errors | _______ in hypothesis testing are possible because decisions are based on probability, it is possible that our conclusions are incorrect |
| type 1 error | incorrect rejection of the null hypothesis, concluding there is an effect when in reality there is no effect, false positive |
| alpha | type 1 error = |
| type 2 error | incorrect acceptance of null hypothesis, concluding there is no effect when in reality there is a real effect, false negative |
| probability of replication | the probability that an effect will be seen again if the study is repeated |
| power | the likelihood of getting a statistically significant result, function of sample size and effect size |
| effect size | strength of relationship between two variables |
| sampling distributions | used to determine probability |
| sampling distribution | theoretical distribution that shows the frequency of each possible value of the statistic from sample size n when the null hypothesis is true |
| expected value | mean of sample means = population mean |
| sampling error | for a single sample, the difference between the sample statistic and the population parameter |
| standard error | across multiple samples, average difference between sample statistic and population parameter, standard deviation of sampling distribution, as n increase error decreases |
| standard error of the mean | average distance of the sample means from the population mean |
| central limit theorem | as n gets larger, the sampling distribution of sample means will approach a normal distribution |
| critical value | value of the statistic that has probability equaling alpha, value of the statistics that is at the boundary of the critical region |
| critical region | area of distribution that is beyond the critical value, contains values that have a probability less than or equal to alpha |
| two tailed probability | used when hypotheses are nondirectional, critical region in both tails |
| one tailed probability | used when hypotheses are directional, critical region in one tail |
| frequency | Sampling distribution of r shows the ______ of each possible value of r |
| probability | For any given sample value of r, we can use the sampling distribution of r to determine the _______ that is occurs by chance |
| pearson value table | use to determine critical values of r |
| probability | can use critical values of r to calculate _______ |
| estimation | inferential technique of using sample statistics to estimate a population parameter |
| point estimate | a single value from the sample is used to estimate the population parameter, varies from sample to sample due to sampling error |
| interval estimate | range of values used to estimate the population parameter, sample % +/- margin of error |
| confidence interval | a range of values that has a specific probability of containing the value of the population parameter |
| confidence interval of the mean | a range that is expected to contain the value of the population mean |
| confidence | the probability that the interval contains the true value of the parameter |
| 1.96 | rage of values with 95% probability of containing population value use z score of |
| 2.58 | rage of values with 99% probability of containing population value use z score of |
| sample statistic +/- margin of error | confidence interval for a population parameter equals |
| z score times s/sqN | margin error equation |
| increases | As confidence level increases, confidence width |
| decreases | As sample size increases, confidence width |
| increases | As standard deviation increases, confidence width |
| true value | % confident that the specified interval contains the ______ of the population parameter |
| not statistically significant | if the confidence interval of the mean difference includes the value of 0 then we conclude that the mean difference is |
| no correlation | if the CI of r includes the value of 0 then we conclude there is |
| significant difference | if confidence intervals of each group do not overlap then we can conclude there is a |
| need a t test | if confidence intervals overlap then |
| t test | used when the DV is continuous and the sample result consists of one or two means |
| t | a statistic that measures the mean difference in standard deviation units |
| one sample t test | used to determine if a single sample mean is different from a known population mean |
| related samples t test | paired samples, two groups are connected in some way that the two group means are related |
| repeated measures | one group of participants is measured twice, such as before and after treatment, within subjects design |
| matched subjects | there are two groups of participants, and each participant in one group is paired with a subject in the other group |
| independent samples t test | used to determine if means from 2 unrelated groups are significantly different, between subjects design |
| separation | T indicates ______ between groups |
| larger | greater the separation the _____ the t |
| M1-M2/SE | Independent samples t test equation |
| Md/SE | related samples t test equation |
| probability | use sampling distribution of t to determine the ______ of an obtained value of t |
| normality | scores are normally distributed in each of the populations from which samples are taken |
| independence of observations | each score in the group does not affect and is not affected by any other score in the group |
| homogeneity of variance | assumption applies to independent samples t test only, populations from which the samples are taken have equal variability |
| robust | not affected much by violations of assumptions |
| proportion of variance, cohens d | two approaches when you have significant t test result |
| t2/t2+df | proportion of variance explained equation for t test |
| cohens d | measure of effect size that assesses the difference between two means in terms of standard deviation |
| 0.2 | d = _____ 85% overlap, small effect size |
| 0.5 | d = _____ 67% overlap, medium effect size |
| 0.8 | d = _____ 53% overlap, large effect size |
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