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PSYC295 - Test 2
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Terms in this set (56)
Type I Error
An error that occurs when a researcher concludes that the independent variable had an effect on the dependent variable, when no such relation exists; a "false positive" REJECTING THE NULL WHEN THEY SHOULD FAIL TO REJECT THE NULL (NO SIGNIFICANT CHANGE) Larger sample does not reduce the risk!
Type II Error
An error that occurs when a researcher concludes that the independent variable had no effect on the dependent variable, when in truth it did; a "false negative" FAILING TO REJECT THE NULL WHEN THEY SHOULD REJECT THE NULL (SIGNIFICANT CHANGE)
50.00+33.49=0.8849
A vertical line is drawn through a normal distribution at z = 1.20. What proportion of the distribution is on the left-hand side of the line?
A normal distribution has μ = 80 and σ = 10. What is the probability of randomly selecting a score greater than 75 from this distribution?
50.00+19.15=69.15
What z-score values form the boundaries for the middle 60% of a normal distribution?
+0.84 & -0.84 because 30% mean to z value
What z-score value separates the top 10% of a normal distribution from the bottom 90%
1.28 because 10% in tail
For a normal distribution, what is the proportion in the tail beyond z = -2.00?
0.0228
σ = 12. SE of 3, then the sample size is n = 4?
No because σM = σ/Sq of n so 12/2=6.
μ =
expected value for the distribution of sample means
How big should a sample be for normal distribution?
30+
Increasing sample size
decreases the standard error and has no effect on the risk of a Type I error.
Describe the relationship: the a-level, the size of the critical region, and the risk of a Type I error.
a-level ^, critical region ^, Type I error risk ^
Whenever you reject the null, you risk a Type II error.
No.
n = 9 M = 66 μ = 75 σ = 12 two-tailed test with an alpha of .05
SE = 4.00 and z=-2.25. Reject the null (significant difference)
n = 36 M = 4.9 μ = 4.1 σ = 1.8 two-tailed test with alpha = .05
pos/neg 1.96 SE = .30 and z = 2.67. Reject the null (significant difference)
Random sample
a sample in which every element in the population has an equal chance of being selected
convenience sample
only members of the population who are easily accessible are selected
generalizability
the extent to which findings can be applied to the larger population from which the sample is drawn
replication
Repeating the essence of a research study, usually with different participants in different situations, to see whether the basic finding extends to other participants and circumstances
volunteer sample
This exists when people volunteer to be part of a study. The problem with this is that it tends to overrepresent people with strong opinions.
personal probability
when the probability is subjective and represents your personal degree of belief
probability
the likelihood that a particular event will occur
=Successes/trials
expected relative-frequency probability
The likelihood of an event occurring based on the actual outcome of many, many trials
trial
Refers to the occasion that a given procedure is carried out.
outcome
a possible result of an experiment.
success
In reference to probability, refers to the outcome for which we're trying to determine probability
control group
the group that does not receive the experimental treatment.
experimental group
subjects in an experiment to whom the independent variable is administered
null hypothesis
A statement , The hypothesis that states there is no difference between two or more sets of data.
research hypothesis
States that a relationship or direction exists between variables. Is scientific, substantive, theoretical, & declarative.
normal curve
a symmetrical curve representing the normal distribution
standardization
defining meaningful scores by comparison with the performance of a pretested group
z score
a measure of how many standard deviations you are away from the norm (average or mean)
z distribution
The number of standard deviations away a random variable is from the population mean ;
z = (variable - population mean)\(standard deviation)
standard normal distribution
The normal distribution with mean µ = 0 and standard deviation σ = 1. Its ordinary scores are the same as its z-scores.
central limit theorem
The theory that, as sample size increases, the distribution of sample means of size n, randomly selected, approaches a normal distribution.
distribution of means
The distribution of all possible sample means for all possible samples from a population
standard error
the standard deviation of a sampling distribution
z test
The statistical formula to determine the z-score of a particular raw score.
assumption
a characteristic that we ideally require the population from which we are sampling to have so that we can make accurate inferences.
parametric test
is an inferential statistical analysis based on a set of assumptions about the population
nonparametric test
an inferential statistical analysis that is not based on a set of assumptions about the population
robust hypothesis
one that produces fairly accurate results even when the data suggest that the population might not meet some of the assumptions.
critical values
The values that lie exactly on the boundary of the region of rejection.
critical region
the most extreme portion of a distribution of statistical values for the null hypothesis determined by the alpha level (typically 5%)
p level
the probability that the obtained correlation or difference between experimental conditions would be expected by chance.
statistically significant
an observed effect so large that it would rarely occur by chance, an observed effect so large that it would rarely occur by chance
one-tailed test
when region of rejection is entirely under one tail of the distribution
two-tailed test
extreme test statistic is in either tail of distribution +/-
Risks of sampling & probability
sample might not represent larger pop
we might not know it's misleading
we might reach inaccurate conclusions
we might make decisions based on bad info
rewards of sampling & probability
sample represents a larger pop
increase our level of confidence in results
accurate conclusions/low cost
remain open minded - know samples can mislead
make wiser decisions based on evidence
Calculating probability
1. determine total # of trials
2. Determine # of successful out comes
3. Divide: # of successes/total # of trials
Gamblers fallacy
mistaken notion that the probability of a particular event changes with a long stream of the same event.
probability is not certainty unless it's 1 or 0
long run patterns, not guarantees.
reject the null/null hypothesis
you found a difference. no mean change or difference. fail to reject the null hypothesis. non directional = two tailed
fail to reject/research hypothesis
you did not find a difference. mean change or difference, reject the null hypothesis. directional = one tailed
Central limit theorem
The theory that, as sample size increases, the distribution of sample means of size n, randomly selected, approaches a normal distribution.
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