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(shapes of distributions 1) Frequency tables
-a frequency table shows how often each value of the variable occurs
*how many people belong to a certain group
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(shapes of distributions 1) Frequency polygon
-visual representation of info contained in a frequency table
- -the way score frequencies are distributed with respect to the values of the variable
- *it can take on different forms
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(shapes of distributions 1) Unimodal distributions
-mode of distribution refers to the most frequently occuring score
-in this distirbution, one score occurs much more frequently than others
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(shapes of distributions 1) Multimodal distributions
-more than one mode exists (or approx. so)
-2 modes exist
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(shapes of distributions 1) Symmetrical distribution
- -is balanced
- *if we cut it in half both sides will be equal

-normal distribution resembles a bell
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(shapes of distributions 1) Skewed distributions
- - is unbalanced, has more values on one end than the rest

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(shapes of distributions 1)( skewed distributions) Negative Skew
-it is heaveir on the larger quantity side, ending in the lighter side
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(shapes of distributions 1)( skewed distributions) Positive skew
it is heavier on the lighter side and ending on the larger side
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(measures of central tendency) Central tendency
most typical or common score
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(measures of central tendency) Mode
most frequently occuring score
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(measures of central tendency) Median
the value at which 1/2 of the ordered scores falls above and 1/2 of the scores fall below
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(measures of central tendency) mean
balancing point of a set of scores; average score
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(measures of central tendency) mode=median=mean
- normal distribution
- *bell-shaped in the center
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(measures of central tendency) mode<median< mean
-scores are positively skewed
- mean is dragged in direction of skew
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(measures of central tendency) mode>median>mean
negatively skewed.
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(standard deviation) Spread or dispersion
degree to which there are variation in the scores
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(standard deviation) Standard deviation
- an index that is used as common way of quantifying
- dispersion

- -SD is an average that can be interpreted as the average amount of dispersion around the mean
- *larger SD= more dispersion
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Interpreting the SD number 2.6..
exp) peoples scores are usually less or more than 2.6 units away from mean on average .
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(correlation) How can we quantify the linear relationship between 2 variables?
-using a common way called correlation coefficient (r)
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(correlation) Properties of Correlation coefficeints
- they range between -1 to 1
-value of the correlation conveys information about the form of the relationship between 2 variables
- (r) can be interpreted as the slope of the line that maps relationship between 2 standarized variables
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(correlation properties) (r) conveys information about the form of relationship between 2 variables
- r>0= relationship is positive
- r<0= relationship between 2 variables is negative
- r=0= there is no relationship between 2 variables
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(correlation) magnitude of correlations- When is a correlation big vs small?
- -correlation between variables rarely get larger than .30
- * variables are influences by many things
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(z-scores) we must interpret mark's grade relative to the average performance of class
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(z-scores) z-scores
- -standarized scores provide a way to express how far a person is from the mean, relative to the variation of the scores

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(z-scores) useful properties of z-scores 1-3
1. mean is always zero for a set of zscores
- 2. the SD of a set of standardized scores is alwasy 1
- *-2-1-0-1-2
- 3. distribution of a set of standardized scores has same shape as the unstandardized scores
- *beware of the normalizaton misinterpretation
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(zscores) useful properties of zscores 4-5
4. standard scores may be used to compute centile scores
5. z-scores provide way to standardize different metrics*different varaibales expressed as zscores can be used under same metric(zscore)
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LArger groups of people are called
- population
- *this is what we aim in our research
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when we conduct a study, we can only study a limited group which is called...
sample
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Sampling error
- -difference we observe as a result of studying a sample of a larger population
- *we are only working with subsets of a large population
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Standard Error of the mean (SEM)
- it tells us how far on average we would expect our sample mean to vary from our expected population mean
- *quantifies amount of smapling error
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Meaning of SEM? ex) SEM of 5
-we can expect the participants to score about 5% from the mean
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(significance tests) How to deal with problem of sampling error in psych research?
is by using larger sample sizes
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(significance tests) formal methods of discussing if an observed effect is greater than what we would expect due to sampling error alone...
null hypothesis significance tests
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(significance tests) what criteria do we use to determine if a something is unlikely to occur by sampling error alone?
- -scientists agree that something is unlikely to be due to chance if it is likely to occur less than 5% of the time
- *does not mean it could not be due to chance, just unlikely
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(significance tests) IF statistic exceeds critical value..
- reject null hypothesis
- *results support research hypothesis
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(significance tests) If stat does not exceed critical value...
- retain null
- *results are likely due to sampling error
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(sample t-test) if calculated t does not exceed critical value...
we fail to reject the null hypothesis
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(sample t-test) Our calcualted risk should fall on the tail ends of graph
-it will indicate it occurs less than 5 %.
- if it falls on the middle than the null hypothesis will be supported
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(inferential errors) Type I error
- -reject the null hypothesis when it is actually true
- *accept as "real" an effect that is due to chance only
- *error determined by choice of critical value (.5,.1.001)
- *worst error
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(inferential errors) Type II errors
- -accept null hypothesis when it is actually false
- *assume that a real effect is only due to chance
- *error influenced by alpha
- *way to prevent it, is to collect sufficient sample size
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4 common ways of null hypothesis significance tests
- -t-tests
- -analysis of varience (ANOVA)
- -z-tests
- -chi-squares
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