## Glossary of Psych Statistics

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- 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 score-mean)

-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

- Non-Normal

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

- Non-Normal

(Ogive Curve) - s-shaped curve. either all high scores or all low scores.

- Non-Normal

(Bimodal Curve) - m-shaped. has two modes.

- Non-Normal

(U-shaped Curve) - U-shaped. 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))

- z-score 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.

- z-score formula
- z= (x-xbar)/ standard dev.

- example of z-score

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

(t-test) - used when control group represents the entire pop.

- omega-squared
- correlation between IV and DV= helps detect interactions between variables

- chi-squared
- compares observed outcomes(data) with expected outcomes (Ho)