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Psychology 200 Exam 2
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Terms in this set (45)
Convenience Sample
is one that uses participants who are readily available
Generalizability
the ability to apply findings from one sample or in one context to other samples or contexts
Replications
duplications of results but with different context or sample characteristics
Random Assignment
all participants have an equal chance of being assigned to any level of the independent variable
Confirmation Bias
only attending to evidence that confirms our beliefs
Illusory correlation
believing an association between variables exists when it does not
Probability
likelihood of an event occurring out of all possible events
subjective intrepretations
personal probability
objective interpretations
expected relative frequency probability
Independence
outcome of each trial is unrelated to outcome of previous trials
Null Hypothesis
There is no difference between levels, no relationship between variables
Research Hypothesis
There is a difference between levels, relationship between variables
Type 1 errors
Sins of commission - rejecting the null hypothesis when it is true
Type 2 errors
Sins of omission - failing to reject the null hypothesis when it is false
standardization
allows comparisons by creating a common share distribution
z-distribution
normal distribution of standardized scores
z-score
number of standard deviations away from the mean of a particular score
z-score equation
z = (X-u)/o
Labels:
Population mean
Population s. d.
Sample Mean
Standard error
-mu
-sigma
-M
-sigma m
parametric test
inferential statistical test based on assumptions about a population
nonparametric test
inferential statistical test not based on assumptions about the population
Hypothesis Test Step 1
Identify
-Populations
-Comparison distribution
-Assumptions
Hypothesis Test Step 2
State the null and research hypotheses.
(Null: Sample = population
Res: Sample /= population)
Hypothesis Test Step 3
Determine the characteristics of the comparison distribution
(Write down the numbers/symbols that describe step 1 and 2)
Hypothesis Test Step 4
Determine critical values and cut off scores
Hypothesis Test Step 5
Calculate the test statistic
Hypothesis Test Step 6
Make a decision
(Reject the null or fail to reject the null)
cut off scores
scores beyond which we would reject the null hypothesis
critical region
area of the distribution where we would reject the null hypothesis
One-tailed test
you predict a change in scores either up or down.
Two-tailed test
you predict a change in scores but not a direction
Point estimate
summary statistic - one number as an estimate of the population
Interval estimate
based on our sample statistic, range of sample statistics we would expect if we repeatedly sampled from the same population
Confidence Intervals
the range around the mean when we add or subtract a margin of error
Confidence interval formula
Mlower = -z(standard error) + Msample
Mupper = z(standard error) + Msample
Effect Size
size of a difference that is unaffected by sample size
Cohen's d estimate for effect size
d = (M-u)/sigma
Experimental designs
you directly manipulated the independent variable
Quasi-experimental designs
you examined naturally occurring groups
correlational designs
you just observed the independent variable
Positive correlation
association between variables such that high scores on one variable tend to have high scores on the other variable
negative correlation
association between variables such that high scores on one variable tend to have low scores on the other variable
Pearson Correlation Coefficient
a statistic that quantifies a linear relation between two scale variables. (r when sample data. p when population parameter)
reliable measure
one that is consistent
valid measure
one that measures what it was designed or intended to measure
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