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Each level of each independent variable has different subjects.
BetweenSubjects or Independent Group Design

Each subject participates in all levels of all independent
variables.
 WithinSubjects or Repeated Measures
 Group Design

There must be at least two independent variables.
Each subject participates in all levels of one
independent variable but not the other.
Mixed Group Design

when the subjects try to figure out the experiment and then alter their
behavior to either "help" the scientist or even hinder the scientist
Demand Characteristics

Standard Error of the Mean

Standard Error of a Sample

when you reject the null hypothesis when shouldn't have because the null
hypothesis is actually true  there is not difference between your groups.
Type I error

when you fail to reject the null hypothesis when you should have because there really is a
significant difference between your groups.
Type II Error

If the scientific hypothesis predicts a
direction of the results, we say it is a
OneTailed Hypothesis

If the scientific hypothesis does not
predict a direction of the results, we say it is a
TwoTailed Hypothesis

an analysis of an experimental design with one independent variable and a nominal
dependent variable
OneWay ChiSquare


df = k 1
Degrees of freedom for a ChiSquare

f_{e} of a TwoWay ChiSquare

when you have two independent variables and a nominal dependent variable
TwoWay ChiSquare

df = (number of rows 1) x (number of columns 1)
Degrees of Freedom for a TwoWay ChiSquare

If your sample size
is above 1000 (Comparing Sample to a Population
Single Sample ztest

Single Sample ztest formula

If your sample size
is below 1000 (Comparing Sample to Population)
Single Sample ttest

Single Sample ttest formula

If your two sample groups are independent of each other
ttest for Independent Groups

ttest for Independent Groups formula

Standard Error of the Difference for Independent Groups

(n1  1) + (n2  1)
df independent groups

If the two samples are not independent of each other but
instead are positively correlated to each other
ttest for Correlated Groups

 the standard
 error of the difference
 (correllated groups)

number of pairs  1
df correlated groups

ttest for correlated samples: using raw data

D bar
 The mean of all the
 difference scores. Difference scores are calculated by subtracting each Y value
 from its X pair value

Standard Difference for Correlated Groups using the raw data

F = MSbg / MSwg
F ratio formula OneWay ANOVA

MSbg = SSbg / dfbg
MSbg formula OneWay ANOVA

MSwg = SSwg / dfwg
MSwg formula OneWay ANOVA

dfbg = k  1
dfbg formula OneWay ANOVA

dfwg =
(n1  1) + (n2  1) + . . . + (nk  1)
dfwg formula OneWay ANOVA

SSbg = [ (ΣX1)2 / n1 ) + (ΣX2)2 / n2 ) + . . . + (ΣXk)2 / nk ) ]  [ (ΣX1 + ΣX2 + . . . + ΣXk )2 / Ntotal ]
SSbg formula OneWay ANOVA

SSwg = [ (ΣX21 + ΣX22 + . . . + ΣX2k ) ]  [ (ΣX1)2 / n1 ) + (ΣX2)2 / n2 ) + . . . + (ΣXk)2 / nk ) ]
SSwg formula OneWay ANOVA



Nominal Dependent Variable Data
ChiSquare (X^{2})

Ordinal Dependent Variable Data
An ordinal statistic

Interval/Ratio Dependent Variable
2+ Factors (Independent Variables)
TwoWay ANOVA

Interval/Ratio Dependent Variable
1 Factor (Independent Variables)
2 Levels (i.e. control and experiment)
TTest

Interval/Ratio Dependent Variable
1 Factor (Independent Variables)
3+ Levels (i.e. control, experiment_{1}, experiment_{2})
OneWay ANOVA

