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95 terms

Psychology Stats Exam 2

STUDY
PLAY
A researcher is seeing if the level of satisfaction with college is different among students who participate in school activities compared to students who do not participate in school activities. Identify the dependent and independent variable in this study.
IV: participation in activities, DV: level of satisfaction
What are the two hypotheses that you state in step 1 of the hypothesis testing procedure called?
null hypothesis (h0), research hypothesis (h1)
Hypothesis testing is used with what general type of statistics?
inferential
In step 2 of the hypothesis testing procedure you determine the characteristics of the comparison distribution. Which population is the comparison distribution: Population 1 or Population 2?
population 2
To determine statistical significance you compare the z-score of your sample from Population 1 with what?
cutoff sample scores/critical values; z-scores of % looked up in tail
Indicate whether you would use a one-tailed or two-tailed test for the following hypothesis: Netflix had fewer subscribers after they raised their prices.
1-tailed
Why do researchers tend to use two-tailed tests rather than one-tailed tests?
one-tailed test can make you miss a statistical significance if it on the other end of spectrum
State the following research hypothesis in symbols: Nebraska Wesleyan students have higher ACT scores than UNL students.
u1 > u2
In which of the 5-steps of hypothesis testing do you indicate whether you test is one- or two-tailed?
step 3
What type of distribution has more variance?
distribution of individuals
A distribution of means is most likely to have what shape?
normal
When your sample consists of a group of people (population 1) why can you not compare that to a distribution of individuals (population2)?
because you must compare like with like and not groups to individuals
Which decision error is generally considered by researchers to be the more serious error
type I
Why is there always the possibility when using statistics that our conclusions will be wrong?
there is always some chance of error because we are never 100% sure
The significance level for a particular statistical test is .05. Which type of decision error will also have a 5% likelihood of occurring
Type I
True or False? You can make both a Type I and Type II error in the same study?
false
A psychologist who states the hypothesis that there will be no difference between a new approach to therapy and the standard approach is stating a
null hypothesis
When a psychologist rejects the null hypothesis at the .05 level, the result of a study indicate that
there is less than a 5% chance of getting such an extreme result by chance if the null hypothesis is true
A result is considered statistically significant when
the sample score is so extreme that the null hypothesis is rejected
The decision to reject the null hypothesis using the Z test is made
by comparing the Z score needed to reject the null hypothesis to the actual Z score of the sample
A psychologist who states the hypothesis that a new training program will be more effective than the old training program is stating a
research hypothesis
How would a psychologist test the hypothesis that a new stress reduction program really works?
try to reject the hypothesis that it does not work
The distribution that represents the situation in which the null hypothesis is true is
the comparison distribution
If the cutoff Z score on the comparison distribution is +/- 2.31, a psychologist can reject the null hypothesis if the sample Z score on this distribution is
2.83
When the results of a study are not extreme enough to reject the null hypothesis, a psychologist can conclude with reasonable confidence that
the results are inconclusive
If a health psychologist wants to know if regular exercise improves peoples' health, the type of hypothesis the psychologist would use is
one-tailed because the study is only interest in whether the exercise increases health
The correct argument for using a one-tailed test when there is a clear basis for predicting a result in a given direction is that
it is less conservative in that one-tailed tests make rejecting the null hypothesis easier
A major misuse of significance tests is the tendency to
decide that if a result is opposite to the prediction, the researcher can still do a two-tailed test later
A major misuse of significance tests is the tendency to
decide that if a result is not significant, the null hypothesis is shown to be true
Which of the following statements is the most accurate description of hypothesis testing?
it is a central theme in the statistical analysis of virtually all psychology research
When your sample is a group of people the correct comparison distribution to use is a distribution of means because
comparing the mean of a sample to a distribution of individuals is a mismatch
In principal, a distribution of means can be formed by
randomly selecting samples from a population, finding the mean of each sample, and then graphing the means
The mean of a distribution of means is
the same as the original population mean
The variance of a distribution of means is smaller than the original population variance because
extreme scores are less likely to affect a distribution of means
In general, the shape of a distribution of means tends to be
unimodal & symmetrical
A study involving 10 participants has a known population mean of u=100 and variance of 25. Using the distribution of means, what characteristics of the comparison distribution would you report in Step 2?
mean = 100, variance = 2.5
As the number of people in a sample gets larger, the distribution of means
becomes a better approximation of the normal curve
Under what conditions is it reasonable to assume that distribution of means will follow a normal curve
30 or more individuals involved or distribution of population means is normal
One important advantage of using effect sizes is that
they are standardized scores that make comparisons of different studies easier
The alpha level is
the probability of a type I error
If a study conducted at the .05 significance level has 80% power
alpha = 5%, beta = 20%
In actual practice, the usual reason for determining power before conducting a study is
to determine the number of participants needed to have a reasonable level of power
Type II errors concern psychologists because
they could mean that good theories or useful practical procedures are not used
Setting the significance level cutoff .10 instead of the more usual .05 increases the likelihood of
Type I error
Beta is the probability that
if the research hypothesis is actually true, the experiment would still fail to support it
The statistical method currently used to combine the results of multiple studies is
meta-analysis
A type I error is the result of
incorrectly rejecting the null hypothesis
Power is the probability that
if the research hypothesis is true, the experiment will support it
Failing to reject the null hypothesis when the research hypothesis is true is
type II error
Effect size is
the degree to which an experimental manipulation separates two populations
If the research hypothesis is true, but the study has a low level of power,
the probability that the study will have a significant result is low
Cohen proposed effect size conventions based on the effects observed in psychology research in general because
previously it was difficult to determine how large an effect size was
The conventional levels of significance of 5% and 1%
are a compromise between the risk of making Type I and Type II errors
hypothesis testing
procedure for deciding whether the outcome of a study supports a particular theory
hypothesis
prediction, often based on informational observation, previous research, or theory that s tested in a research study
theory
set of principles that attempt to explain one or more facts, relationships, or events; predictions are derived from theories
research hypothesis
statement in hypothesis testing about the predicted relation between populations
Null hypothesis
statement about a relation between populations that is the opposite of the research hypothesis
comparison distribution
distribution used in hypothesis testing; represents the population situation if the null hypothesis is true
cutoff sample score
in hypothesis testing, the point on the comparison distribution at which, if reached or exceeded by the sample score you reject the null hypothesis
conventional levels of significance
p<.05, p<.01; levels of significance widely used in psychology
statistically significant
conclusion taht the results of the study would be unlikely if in fact the sample population is no different from the general population
directional hypothesis
research hypothesis predicting a particular direction of difference between populations
one-tailed test
hypothesis-testing procedure for a directional hypothesis; region of comparison distribution in which the null hypothesis would be rejected is all on one side of the distribution
Nondirectional hypothesis
research hypothesis that doesn't predict a particular direction of different between populations
Two-tailed test
hypothesis-testing procedure for a nondirectional hypothesis; region of the comparison distribution in which the null hypothesis would be rejected is divided between the two sides of the distribution
distribution of means
distribution of means of samples of a given size from a population; comparison distribution when testing hypotheses involving a single sample of more than one individual
mean of a distribution of means
the means of a distribution of means of samples of a given size from a population; same as u
variance of a distribution of means
variance of the population divided by the number of scores in each sample
standard deviation of a distribution of means
square root of the variance of a distribution of means; aka SEM or SE
decision errors
incorrect conclusion in hypothesis testing in relation to the real situation
Type I error
rejecting the null hypothesis when in fact it is true
Alpha
probability of making a Type I error; same as significance level
Type II error
failing to reject the null hypothesis when in fact it is false
Beta
probability of making a Type II error
Effect Size
in studies involving means of one or two groups, measure of difference between populations
Effect size conventions
standard rules about what to consider a small, medium, & large effect size, based on what is typical in psych research
Meta-analysis
statistical method for combining effect sizes from different studies
Statistical power
probability that a study will give a significant result if the research hypothesis is true
power tables
table for a hypothesis-testing procedure showing the statistical power of studies with various effect sizes & sample sizes
Be able to explain the relationship between effect size and statistical significance in interpreting research results
they are unrelated because effect size is how big the difference is between our means (practical significance) and statistical significance is if the is a difference between our groups (hypothesis testing; decision errors); power is related to both effect size and statistical significance
Independent variable
what is being manipulated, predictor, cause
Dependent Variable
what is being measured, what is being predicted, effect
Steps of Hypothesis Testing
1. Identify populations & hypotheses 2. Determine population mean, population standard deviation 3. Determine cutoff sample score on comparison distribution at which the null hypothesis should be rejected 4. Determine your sample's score 5. Decide whether to reject the null hypothesis
p<.05 are what cutoff scores for 1-tailed & 2-tailed
1.64 & 1.96
<0.2 effect size
no effect
0.2-0.49
small effect
0.5-0.79
medium effect
>/= 0.8
large effect
What determines power?
sample size (larger sample increases power), effect size (larger effect size increases power), significance level (power is higher @.5), one vs. two-tailed test (varies power)
Use of power table
determining # of participants before study, interpreting results of a study
low power with non significant results means
truly inconclusive
high power with non significant results means
unlikely that the research hypothesis is true or small effect
lower power with significant results means
likely practical significance
high power with significant results means
may not be practically significant