stats exam 2 review

what is random sampling

every member of the population has an equal chance of being in the sample

what is convenience sampling

use participants who are readily available

what does the central limit theorem state

as you take more samples and data, it starts to look more like the normal curve

the central limit theorem is a statement about the form of the sampling distribution of

the mean

the central limit theorem states that a sampling distribution of the mean approaches the normal curve if

the sample size is large

as n becomes larger, the standard error becomes

smaller

the t distribution and the normal distribution are alike in that they

have equal proportions to the left of 0.00

To test a skill-building workshop, I administered a scale measuring internal motivation after the workshop to a sample of individuals.  The population mean on this scale is 23--with higher scores indicating greater internal motivation.  My sample mean was 26 and produced a 95% confidence interval of 24.5 to 27.5.  I can conclude that

the confidence interval suggests that the workshop has efficacy in increasing internal motivation

the z distribution is curve (s) while the t distribution is __ curve (s)

one; many

as df increases, the t distribution becomes

more peaked

which confidence interval has the higher probability of capturing the unmeasured population mean

99 percent

confidence intervals and hypothesis testing are categories of

inferential statistics

which confidence interval will produce the smalest range of values: 80%, 95%, 99%, 100%

80%

what t distribution would have the greatest proportion to the right of t = 1

df = 2

a 95th percent confidence interval of 14 to 17 means

95 percent of the confidence intervals calculated like this one will contain the population meanthe t

the t distribution has a mean equal to

zero

if the t test value is less than the critical value on the table, __ the null hypothesis even though you could be making a __ error

retain; type II

data were gathered, the results analyzed, and the null is rejected. based on the logic of the hypothesis testing, you can conclude that

the mean of the population from which the sample was drawn is probably not the mean specified by the null hypothesis

the p in p < .05 is the probability of

the data obtained if the null hypothesis is true

a t test value that is smaller than the tabled value of t

leads to retention of the null hypothesis

a hypothesis was proposed, an alpha level of .05 adopted, data gathered, a mean computed, and a probability figure attached to the mean. which of the probabilities below are in the rejection region?: .001, 1, .5, all of these

.001

what is a condition where you would NOt use null hypothesis significance testing (NHST)

population data are available

in statistics, a significant difference is one that is

probably not due to chance

null hypothesis significance testing (NHST) uses __ data to tell whether the __ came from a(n) __ population

sample; sample; hypothesized

the t distribution, as a sampling distribution, gives the probability of events when

the null hypothesis is true

which alternative is most closely related to the concept of a two-tailed test of significance

a divided rejection region

what is the difference between 99% and 95% confidence intervals

99 is more accurate but less precise, 95 is less accurate but more precise

what are the assumptions made during NHST

random assignment/selection, about variability and distribution in the underlying population, the null is true

what does it mean when you reject the null

p < .05, significant, support the research hypothesis, type I error

what does it mean when you retain the null

p > .05, not significant, suspend any judgement about either hypothesis, type II error

does retaining the null mean you are supporting it

no

does non significant automatically mean no difference

no

what is the rejection region

area of sampling distribution that corresponds to test statistic values that lead to rejection of the null

what are the characteristics of the t distribution

mean is zero, as the size of the difference between the sample and population mean increases so does the t value, as differences and t values become larger probabilities become smaller, small probabilities go with small areas at each end of the curve

when do you use NHST with t distribution

when there is only 1 or 2 samples, when you want to compare means, when there is only one independent variable

when do you use paired vs independent samples

paired (same participants retested) independent (different participants in each sample)