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48 Cards in this Set
 Front
 Back
Descriptive Statistics

method for organizing and describing experimental data


Inferential Statistics

method for making decisions about the significance of experimental results. were the results caused by the IV or just by chance


cumf

the cumulative frequency. the number of scores at or below any given score


cumprop

the proportion of scores at or below any given score ( take the cumf and divide by the total number of scores)


%ile

percentage of scores at or below any given score


x

the data (scores) arranged from lowest to highest


f

the frequency with which each score occurs in the data


mean

mathematical average of a group of scores, the sum of the scores divided by the number of scores
(most vulnerable to extreme scores) 

median

the middle score, at the 50%ile where 1/2 the scores are above and 1/2 the scores are below(best used for skewed distributions)


range

the simplist measure. the difference between the highest and lowest scores in the distribution


Deviation

the difference between a given score and the mean of the distribution D=x  xbar


Variance

the square of a scores deviation from the mean d2=variance.


standard deviation

the avergae deviation of all scores from the mean


calculating standard dev.

1. find mean of distributon
2. create freq. dist. chart x(score) f(freq.) d(deviation, the scoremean) d2(variance, deviation squared) fd2(variance * freq) 1 add up all the numbers in fd2 2 divide dum of fd2 by N 3 take square root of result 

Normal frequency distribution

symmetrical all around
mean=median=mode 

NonNormal
(Skewed Curves) 
skewed to right or left. either few extremly high scores or a few extremly low scores


NonNormal
(Ogive Curve) 
sshaped curve. either all high scores or all low scores.


NonNormal
(Bimodal Curve) 
mshaped. has two modes.


NonNormal
(Ushaped Curve) 
Ushaped. extreme scores on both ends.


Normal
(Leptokurtic Curve) 
small standard dev. (scores all bunched up around mean)


Normal
(Platykurtic) 
large standard deviation (scores very spread out)
((very LOOOOONG) 

Normal
(Mesokurtic) 
classic shape. Standard normal curve.


xbar
+1, +2, +3 numbers 
50%tile
34.13% 13.59% 2.15% .14% 

percentiles can also be seen as...((13.59%))

probabilities ((P=.1359))


zscore defintion

allows us to determine the exact distance of any given score from mean (in standard deviations). All you need is the mean and stand. dev.


zscore formula

z= (xxbar)/ standard dev.


example of zscore
IQs=140 xbar= 200 SD=30 IQmj=60 xbar=30 SD=10 
s=2
mj=+3 

correlation

refers to the type and the strength of the relationship between two variables. Correlation data does not indicate a cause and effect relationship.


correlation coefficient

Pearson's r
+1=perfect/direct + rel.(Aup B up) 0.00=no relationship 1=perfect/indei2rec/inverse/ relationship (Aup B down) 

Logic of inferential statistics

to decide if the difference between the experimental group mean and the control group mean was caused by the IV or just chance


Null Hypothesis (Ho)

Nearly always define the Ho as follows "the difference between the xbar"e" and the xbar"c" is caused due to chance" (Assumption/Theory)
(skepticism) 

Ho is correct
Fail to reject the Ho 
assert difference is due to chance


Ho is incorrect
Reject the Ho 
assert difference is caused by IV


alpha level

the probability of an event occuring by chance at which one is willing to reject the Ho


alpha=.01

p=1 of 100 times (by chance)


alpha=.05

p= 5 of 100 times (by chance)


if Xbar"e" < alpha...

reject Ho
caused by IV 

if xbar"e" > alpha...

fail to reject the Ho
caused by chance 

diff between descriptive/inferential

make decisions with inferential andnot with descriptive. describe with descriptive


if r=+1.00 and variable A is decreasing what is happening to variable b

also decreasing


mean is 10. sd is 2. what is score at z=+2

14


cumulative freq.

number of scores @ or below or @ and above


type 1 error

increases as alpha increases=rejecting the ho when it is true=stating IV caused the result when it was really due to chance


type 2 error

increases as alpha gets smaller=failing to reject t he Ho when it is really false=stating the result was due to chance when was due to IV


ANOVA

stat. use for multiple IV's


students t
(ttest) 
used when control group represents the entire pop.


omegasquared

correlation between IV and DV= helps detect interactions between variables


chisquared

compares observed outcomes(data) with expected outcomes (Ho)
