ANOVA With Full Factorial Experiments
This lesson explains how to use analysis of variance (ANOVA) with balanced, completely randomized, full factorial experiments.
The discussion covers general issues related to design, analysis, and interpretation with
fixed factors and with
random factors.
Future lessons expand on this discussion, using sample problems to demonstrate the analysis
under the following scenarios:
Design Considerations
Since this lesson is all about implementing analysis of variance with a balanced, completely randomized,
full factorial experiment, we begin by answering four relevant questions:
 What is a full factorial experiment?
 What is a completely randomized design?
 What are the data requirements for analysis of variance with a completely randomized, full factorial design?
 What is a balanced design?
What is a Full Factorial Experiment?
A factorial
experiment
allows researchers to study the joint effect of two or more
factors
on a dependent variable.
With a full factorial design, the experiment includes a treatment group for every combination of
factor levels. Therefore, the number of treatment groups is the product of factor levels. For example,
consider the full factorial design shown below:

C_{1} 
C_{2} 
C_{3} 
C_{4} 
A_{1} 
B_{1} 
Grp 1 
Grp 2 
Grp 3 
Grp 4 
B_{2} 
Grp 5 
Grp 6 
Grp 7 
Grp 8 
B_{3} 
Grp 9 
Grp 10 
Grp 11 
Grp 12 
A_{2} 
B_{1} 
Grp 13 
Grp 14 
Grp 15 
Grp 16 
B_{2} 
Grp 17 
Grp 18 
Grp 19 
Grp 20 
B_{3} 
Grp 21 
Grp 22 
Grp 23 
Grp 24 

A_{1} 
A_{2} 
B_{1} 
B_{2} 
B_{3} 
B_{1} 
B_{2} 
B_{3} 
C_{1} 
Group 1 
Group 2 
Group 3 
Group 4 
Group 5 
Group 6 
C_{2} 
Group 7 
Group 8 
Group 9 
Group 10 
Group 11 
Group 12 
C_{3} 
Group 13 
Group 14 
Group 15 
Group 16 
Group 17 
Group 18 
C_{4} 
Group 19 
Group 20 
Group 21 
Group 22 
Group 23 
Group 24 
Factor A has two levels, factor B has three levels, and factor C has four levels. Therefore, the full factorial design has
2 x 3 x 4 = 24 treatment groups.
Full factorial designs can be characterized by the number of treatment levels associated with each factor, or by the
number of factors in the design. Thus,
the design above could be described as a 2 x 3 x 4 design (number of treatment levels) or as a threefactor design
(number of factors).
Note: Another type of factorial experiment is a fractional factorial. Unlike full factorial experiments, which
include a treatment group for every combination of factor levels, fractional factorial experiments include only
a subset of possible treatment groups. Our focus in this lesson is on full factorial experiments, rather than
fractional factorial experiments.
Completely Randomized Design
With a full factorial experiment, a completely randomized design is distinguished by the following attributes:
 The design has two or more factors (i.e., two or more
independent variables), each with two or more
levels.
 Treatment groups are defined by a unique combination of nonoverlapping factor levels.
 The number of treatment groups is the product of factor levels.
 Experimental units are randomly selected from a known population.
 Each experimental unit is randomly assigned to one, and only one, treatment group.
 Each experimental unit provides one dependent variable score.
Data Requirements
Analysis of variance requires that the dependent variable be measured on an
interval scale or a
ratio scale.
In addition, analysis of variance with a full factorial experiment makes three assumptions about dependent variable scores:
 Independence. The dependent variable score for each experimental unit is independent of the score for any other unit.
 Normality. In the population, dependent variable scores are normally distributed within treatment groups.
 Equality of variance. In the population, the variance of dependent variable scores in each treatment group is equal.
(Equality of variance is also known as homogeneity of variance or homoscedasticity.)
The assumption of independence is the most important assumption. When that assumption is violated, the resulting
statistical tests can be misleading. This assumption is tenable when (a) experimental units are randomly
sampled from the population and (b) sampled unitsare randomly assigned to treatments.
With respect to the other two assumptions, analysis of variance is more forgiving.
Violations of normality are less problematic when the sample size is large. And violations of the
equal variance assumption are less problematic when the sample size within groups is equal.
Before conducting an analysis of variance with data from a full factorial experiment,
it is best practice to check for violations of normality and homogeneity assumptions. For further information, see:
Balanced versus Unbalanced Design
A balanced design has an equal number of observations in all treatment groups. In contrast, an
unbalanced design has an unequal number of observations in some treatment groups.
Balance is not required with oneway analysis of variance,
but it is helpful with fullfactorial designs because:
 Balanced factorial designs are less vulnerable to violations of the equal variance assumption.
 Balanced factorial designs have more statistical power.
 Unbalanced factorial designs can produce confounded factors, making it hard to interpret results.
 Unbalanced designs use special weights for data analysis, which complicates the analysis.
Note: Our focus in this lesson is on balanced designs.
Analytical Logic
To implement analysis of variance with a balanced, completely randomized, full factorial experiment,
a researcher takes the following steps:
 Specify a mathematical model to describe how main effects and interaction effects influence the dependent variable.
 Write statistical hypotheses to be tested by experimental data.
 Specify a significance level for a hypothesis test.
 Compute the grand mean and the mean scores for each treatment group.
 Compute sums of squares for each effect in the model.
 Find the degrees of freedom associated with each effect in the model.
 Based on sums of squares and degrees of freedom, compute mean squares for each effect in the model.
 Find the expected value of the mean squares for each effect in the model.
 Compute a test statistic for each effect, based on observed mean squares and their expected values.
 Find the P value for each test statistic.
 Accept or reject the null hypothesis for each effect, based on the P value and the significance level.
 Assess the magnitude of effect, based on sums of squares.
If you are familiar with oneway analysis of variance (see
OneWay Analysis of Variance),
you might notice that the analytical logic for a completelyrandomized, singlefactor experiment
is very similar to the logic for a completely randomized, full factorial experiment. Here are
the main differences:
 Formulas for mean scores and sums of squares differ, depending on the number of factors in the experiment.
 Expected mean squares differ, depending on whether the experiment tests fixed effects and/or random effects.
Below, we'll explain how to implement analysis of variance for fixedeffects models, randomeffects models, and mixed models
with a balanced, twofactor, completely randomized, fullfactorial experiment.
Mathematical Model
For every experimental design, there is a mathematical model that accounts for all of the
independent and extraneous variables that affect the dependent variable.
Fixed Effects
For example, here is the
fixedeffects mathematical model for a twofactor,
completely randomized, fullfactorial experiment:
X_{ i j m} = μ + α_{ i} + β_{ j} + αβ_{ i j} + ε_{ m ( ij )}
where X_{ i j m} is the dependent variable score for subject m in treatment group ij,
μ is the population mean,
α_{ i} is the main effect of Factor A at level i;
β_{ j} is the main effect of Factor B at level j;
αβ_{ i j} is the interaction effect of Factor A at level i and Factor B at level j;
and ε_{ m ( ij )} is the effect of all other extraneous variables on subject m
in treatment group ij.
For this model, it is assumed that ε_{ m ( ij )} is normally and independently
distributed with a mean of zero and a variance of σ_{ε}^{2}.
The mean ( μ ) is constant.
Note: The parentheses in ε_{ m ( ij )} indicate that subjects are
nested under treatment groups. When a subject is assigned to only one treatment group,
we say that the subject is nested under a treatment.
Random Effects
The randomeffects mathematical model for a completely randomized
full factorial experiment is similar to the fixedeffects mathematical model. It can also be expressed as:
X_{ i j m} = μ + α_{ i} + β_{ j} + αβ_{ i j} + ε_{ m ( ij )}
Like the fixedeffects mathematical model, the randomeffects model also assumes that (1) ε_{ m ( ij )} is normally and independently
distributed with a mean of zero and a variance of σ_{ε}^{2} and (2) the mean ( μ ) is constant.
Here's the difference between the two mathematical models.
With a fixedeffects model, the experimenter includes all treatment levels of interest in the experiment. With a randomeffects model,
the experimenter includes a random sample of treatment levels in the experiment. Therefore, in the randomeffects mathematical model,
the following is true:
 The main effect ( α_{ i} ) is a random variable with a mean of zero and a variance of
σ^{2}_{α}.
 The main effect ( β_{ j} ) is a random variable with a mean of zero and a variance of
σ^{2}_{β}.
 The interaction effect ( αβ_{ ij} ) is a random variable with a mean of zero and a variance of
σ^{2}_{αβ}.
All three effects are assumed to be normally and independently distributed (NID).
Statistical Hypotheses
With a full factorial experiment, it is possible to test all main effects and all interaction effects.
For example, here are the null hypotheses (H_{0}) and
alternative hypotheses (H_{1}) for
each effect in a twofactor full factorial experiment.
Fixed Effects
For fixedeffects models, it is common practice to write statistical hypotheses in terms of treatment effects:
H_{0}: α_{ i} = 0 for all i 
H_{0}: β_{ j} = 0 for all j 
H_{0}: αβ_{ ij} = 0 for all ij 
H_{1}: α_{ i} ≠ 0 for some i 
H_{1}: β_{ j} ≠ 0 for some j 
H_{1}: αβ_{ ij} ≠ 0 for some ij 
Random Effects
For randomeffects models, it is common practice to write statistical hypotheses in terms of the variance of treatment levels
included in the experiment:
H_{0}: σ^{2}_{α} = 0 
H_{0}: σ^{2}_{β} = 0 
H_{0}: σ^{2}_{αβ} = 0 
H_{1}: σ^{2}_{α} ≠ 0 
H_{1}: σ^{2}_{β} ≠ 0 
H_{1}: σ^{2}_{αβ} ≠ 0 
Significance Level
The significance level (also known as alpha or α) is the probability of rejecting the null hypothesis when it
is actually true. The significance level for an experiment is specified by the experimenter, before data collection
begins. Experimenters often choose significance levels of 0.05 or 0.01.
A significance level of 0.05 means that there is a 5% chance of rejecting the null hypothesis
when it is true. A significance level of 0.01 means that there is a 1% chance of rejecting the null hypothesis
when it is true. The lower the significance level, the more persuasive the evidence needs to be
before an experimenter can reject the null hypothesis.
Mean Scores
Analysis of variance for a full factorial experiment begins by computing a grand mean,
marginal means, and group means.
Here are formulas for computing the various means for a balanced, twofactor, full factorial experiment:
 Grand mean. The grand mean (X) is the mean of all observations,
computed as follows:
X = ( 1 / N )
pΣi=1
qΣj=1
nΣm=1
( X
_{ i j m} )
 Marginal means for Factor A. The mean for level i of Factor A is computed as follows:
X_{ i} = ( 1 / q )
qΣj=1
nΣm=1
( X
_{ i j m} )
 Marginal means for Factor B. The mean for level j of Factor B is computed as follows:
X_{ j} = ( 1 / p )
pΣi=1
nΣm=1
( X
_{ i j m} )
 Group means. The mean of all observations in group i j
( X_{ i j} ) is computed as follows:
X_{ i j} = ( 1 / n )
nΣm=1
( X
_{ i j m} )
In the equations above, N is the total sample size across all treatment groups;
n is the sample size in a single treatment group,
p is the number of levels of Factor A, and q is the number of levels of Factor B.
Sums of Squares
A sum of squares is the sum of squared deviations from a mean score. Twoway analysis of variance makes use of five sums of squares:
 Factor A sum of squares. The sum of squares for Factor A (SSA) measures variation of the marginal means
of Factor A ( X_{ i} )
around the grand mean ( X ). It can be computed from the following formula:
SSA = nq
pΣi=1
(
X_{ i} 
X )
^{2}
 Factor B sum of squares. The sum of squares for Factor B (SSB) measures variation of the marginal means
of Factor B ( X_{ j} )
around the grand mean ( X ). It can be computed from the following formula:
SSB = np
qΣj=1
(
X_{ j} 
X )
^{2}
 Interaction sum of squares. The sum of squares for the interaction between Factor A and Factor B (SSAB)
can be computed from the following formula:
SSAB = n
pΣi=1
qΣj=1
(
X_{ i j}

X_{ i}

X_{ j}
+
X )
^{2}
 Withingroups sum of squares. The withingroups sum of squares (SSW) measures variation of all scores
( X_{ i j m} ) around their respective group means
( X _{i j} ).
It can be computed from the following formula:
SSW =
pΣi=1
qΣj=1
nΣm=1
( X
_{ i j m} 
X _{i j} )
^{2}
Note: The withingroups sum of squares is also known as the error sum of squares (SSE).
 Total sum of squares. The total sum of squares (SST) measures variation of all scores
( X_{ i j m} ) around the grand mean
( X ).
It can be computed from the following formula:
SST =
pΣi=1
qΣj=1
nΣm=1
( X
_{ i j m} 
X )
^{2}
In the formulas above, n is the sample size in each treatment group, p is the number of levels of Factor A,
and q is the number of levels of Factor B.
It turns out that the total sum of squares is equal to the sum of the component sums of squares, as shown below:
SST = SSA + SSB + SSAB + SSW
As you'll see later on, this relationship will allow us to assess the relative magnitude of any effect
(Factor A, Factor B, or the AB interaction) on the dependent variable.
Degrees of Freedom
The term degrees of freedom (df) refers to the number of independent sample points used to compute a
statistic minus the number of
parameters estimated from the sample points.
The degrees of freedom used to compute the various sums of squares for a balanced, twoway factorial experiment
are shown in the table below:
Sum of squares 
Degrees of freedom 
Factor A 
p  1 
Factor B 
q  1 
AB interaction 
( p  1 )( q  1) 
Within groups 
pq( n  1 ) 
Total 
npq  1 
Notice that there is an additive relationship between the various sums of squares. The degrees of freedom
for total sum of squares (df_{TOT}) is equal to the degrees of freedom for the Factor A sum of squares (df_{A}) plus
the degrees of freedom for the Factor B sum of squares (df_{B}) plus
the degrees of freedom for the AB interaction sum of squares (df_{AB}) plus
the degrees of freedom for withingroups sum of squares (df_{WG}). That is,
df_{TOT} = df_{A} + df_{B} + df_{AB} + df_{WG}
Mean Squares
A mean square is an estimate of population variance. It is computed by dividing
a sum of squares (SS) by its corresponding degrees of freedom (df), as shown below:
MS = SS / df
To conduct analysis of variance with a twofactor, full factorial experiment, we are interested in four mean squares:
 Factor A mean square. The Factor A mean square ( MS_{A} ) measures
variation due to the main effect of Factor A. It can be computed as follows:
MS_{A} = SSA / df_{A}
 Factor B mean square. The Factor B mean square ( MS_{B} ) measures
variation due to the main effect of Factor B. It can be computed as follows:
MS_{B} = SSB / df_{B}
 Interaction mean square. The mean square for the AB interaction measures variation due to
the AB interaction effect. It can be computed as follows:
MS_{AB} = SSAB / df_{AB}
 Within groups mean square. The withingroups mean square ( MS_{WG} ) measures
variation due to differences among experimental units within the same treatment group. It can be computed as follows:
MS_{WG} = SSW / df_{WG}
Expected Value
The expected value
of a mean square is the average value of the mean square over a large number of experiments.
Statisticians have derived formulas for the expected value of mean squares for balanced, twofactor, full factorial
experiments. The expected values differ, depending on whether the experiment uses all fixed factors,
all random factors, or a mix of fixed and random factors.
FixedEffects Model
A fixedeffects model describes an experiment in which all factors are fixed factors.
The table below shows the expected value of mean squares for a balanced, twofactor, full factorial
experiment when both factors are fixed:
Mean square 
Expected value 
MS_{A} 
σ^{2}_{WG} + nqσ^{2}_{A} 
MS_{B} 
σ^{2}_{WG} + npσ^{2}_{B} 
MS_{AB} 
σ^{2}_{WG} + nσ^{2}_{AB} 
MS_{WG} 
σ^{2}_{WG} 
In the table above, n is the sample size in each treatment group,
p is the number of levels for Factor A,
q is the number of levels for Factor B,
σ^{2}_{A} is the variance of main effects due to Factor A,
σ^{2}_{B} is the variance of main effects due to Factor B,
σ^{2}_{AB} is the variance due to interaction effects, and
σ^{2}_{WG} is the variance due to extraneous variables (also known as variance due to experimental error).
RandomEffects Model
A randomeffects model describes an experiment in which all factors are random factors.
The table below shows the expected value of mean squares for a balanced, twofactor, full factorial
experiment when both factors are random:
Mean square 
Expected value 
MS_{A} 
σ^{2}_{WG} + nσ^{2}_{AB} + nqσ^{2}_{A} 
MS_{B} 
σ^{2}_{WG} + nσ^{2}_{AB} + npσ^{2}_{B} 
MS_{AB} 
σ^{2}_{WG} + nσ^{2}_{AB} 
MS_{WG} 
σ^{2}_{WG} 
Mixed Model
A mixed model describes an experiment in which at least one factor is a fixed factor, and at least one factor is a random factor.
The table below shows the expected value of mean squares for a balanced, twofactor, full factorial
experiment, when Factor A is a fixed factor and Factor B is a random factor:
Mean square 
Expected value 
MS_{A} 
σ^{2}_{WG} + nσ^{2}_{AB} + nqσ^{2}_{A} 
MS_{B} 
σ^{2}_{WG} + npσ^{2}_{B} 
MS_{AB} 
σ^{2}_{WG} + nσ^{2}_{AB} 
MS_{WG} 
σ^{2}_{WG} 
Note: The expected values shown in the tables are approximations. For all practical purposes, the
values for the fixedeffects model will always be valid for computing test statistics (see below). The values for
the randomeffects model and the mixed model will be valid when randomeffect levels in the experiment
represent a small fraction of levels in the population.
Test Statistics
Suppose we want to test the significance of a main effect or the interaction effect in a twofactor,
full factorial experiment. We can use the mean squares to define a test statistic F as follows:
F(v_{1}, v_{2}) = MS_{EFFECT 1} / MS_{EFFECT 2}
where MS_{EFFECT 1} is the mean square for the effect we want to test;
MS_{EFFECT 2} is an appropriate mean square, based on the expected value of mean squares;
v_{1} is the degrees of freedom for MS_{EFFECT 1} ;
and v_{2} is the degrees of freedom for MS_{EFFECT 2}.
How do you choose an appropriate mean square for the denominator in an F ratio?
The expected value of the denominator of the F ratio should be identical
to the expected value of the numerator, except for one thing: The numerator should have an extra
term that includes the variance of the effect being tested (σ^{2}_{EFFECT}).
FixedEffects Model
The table below shows how to construct F ratios when an experiment uses a fixedeffects model.
Table 1. FixedEffects Model
Effect 
Mean square: Expected value 
F ratio 
A 
σ^{2}_{WG} + nqσ^{2}_{A} 

B 
σ^{2}_{WG} + nqσ^{2}_{B} 

AB 
σ^{2}_{WG} + nσ^{2}_{AB} 

Error 
σ^{2}_{WG} 

RandomEffects Model
The table below shows how to construct F ratios when an experiment uses a Randomeffects model.
Table 2. RandomEffects Model
Effect 
Mean square: Expected value 
F ratio 
A 
σ^{2}_{WG} + nσ^{2}_{AB} + nqσ^{2}_{A} 

B 
σ^{2}_{WG} + nσ^{2}_{AB} + npσ^{2}_{B} 

AB 
σ^{2}_{WG} + nσ^{2}_{AB} 

Error 
σ^{2}_{WG} 

Mixed Model
The table below shows how to construct F ratios when an experiment uses a mixed model. Here, Factor A
is a fixed effect, and Factor B is a random effect.
Table 3. Mixed Model
Effect 
Mean square: Expected value 
F ratio 
A (fixed) 
σ^{2}_{WG} + nσ^{2}_{AB} + nqσ^{2}_{A} 

B (random) 
σ^{2}_{WG} + npσ^{2}_{B} 

AB 
σ^{2}_{WG} + nσ^{2}_{AB} 

Error 
σ^{2}_{WG} 

How to Interpret F Ratios
For each F ratio in the tables above, notice that numerator should equal the denominator
when the variation due to the source effect ( σ^{2}_{ SOURCE} ) is zero (i.e., when the source does not affect the
dependent variable). And the numerator should be bigger than the denominator
when the variation due to the source effect is not zero (i.e., when the source does affect the
dependent variable).
Defined in this way, each F ratio is a convenient measure that we can use to test the null hypothesis about
the effect of a source (Factor A, Factor B, or the AB interaction) on the dependent variable. Here's how
to conduct the test:
 When the F ratio is close to one, the numerator of the F ratio is approximately equal to the denominator.
This indicates that the source did not affect the dependent variable, so we cannot
reject the null hypothesis.
 When the F ratio is significantly greater than one, the numerator is bigger than the denominator.
This indicates that the source did affect the dependent variable, so we must reject the null hypothesis.
What does it mean for the F ratio to be significantly greater than one?
To answer that question, we need to talk about the Pvalue.
PValue
In an experiment, a Pvalue is the probability of obtaining a result more extreme than the observed experimental outcome,
assuming the null hypothesis is true.
With analysis of variance for a full factorial experiment, the F ratios are the observed experimental outcomes that we are interested in.
So, the Pvalue would be the probability that an F ratio would be more extreme (i.e., bigger) than the
actual F ratio computed from experimental data.
How does an experimenter attach a probability to an observed F ratio?
Luckily, the F ratio is a random variable
that has an F distribution.
The degrees of freedom (v_{1} and v_{2}) for the F ratio are the degrees of freedom associated with the effects
used to compute the F ratio.
For example, consider the F ratio for Factor A when Factor A is a fixed effect. That F ratio (F_{A}) is computed from
the following formula:
F_{A} = F(v_{1}, v_{2}) = MS_{A} / MS_{WG}
MS_{A} (the numerator in the formula) has degrees of freedom equal to df_{A }; so for F_{A }, v_{1} is equal to
df_{A }. Similarly, MS_{WG} (the denominator in the formula) has degrees of freedom equal to df_{WG }; so
for F_{A }, v_{2} is equal to df_{WG }.
Knowing the F ratio and its degrees of freedom, we can use an F table or an online calculator to find the probability that
an F ratio will be bigger than the actual F ratio observed in the experiment.
F Distribution Calculator
To find the Pvalue associated with an F ratio, use Stat Trek's free
F distribution calculator. You can
access the calculator by clicking a link in the table of contents (at the top of this web page in the left column).
find the calculator in the Appendix section of the table of contents, which can be
accessed by tapping the "Analysis of Variance: Table of Contents" button at the top of the page.
Or you can
click
tap
the button below.
F Distribution Calculator
For examples that show how to find the Pvalue for an F ratio, see
Problem 1 or Problem 2 at the end of this lesson.
Hypothesis Test
Recall that the experimenter specified a significance level early on  before the first data point was collected.
Once you know the significance level and the Pvalues, the hypothesis tests are routine.
Here's the decision rule for accepting or rejecting a null hypothesis:
 If the Pvalue is bigger than the significance level, accept the null hypothesis.
 If the Pvalue is equal to or smaller than the significance level, reject the null hypothesis.
A "big" Pvalue for a source of variation (Factor A, Factor B, or the AB interaction)
indicates that the source did not have a statistically significant effect
on the dependent variable. A "small" Pvalue indicates that the source did have a statistically significant
effect on the dependent variable.
Magnitude of Effect
The hypothesis tests tell us whether sources of variation in our experiment had a statistically
significant effect on the dependent variable, but the tests do not address the magnitude
of the effect. Here's the issue:
 When the sample size is large, you may find that even small effects (indicated by a small F ratio) are
statistically significant.
 When the sample size is small, you may find that even big effects are
not statistically significant.
With this in mind, it is customary to supplement analysis of variance with an appropriate measure
of effect size. Eta squared (η^{2}) is one such measure. Eta squared is the proportion of variance in the
dependent variable that is explained by a treatment effect. The eta squared formula
for a main effect or an interaction effect is:
η^{2} = SS_{EFFECT} / SST
where SS_{EFFECT} is the sum of squares for a particular treatment effect (i.e.,
Factor A, Factor B, or the AB interaction) and SST is the total sum of squares.
ANOVA Summary Table
It is traditional to summarize ANOVA results in an analysis of variance table. Here,
filled with hypothetical data, is an analysis of variance table for a 2 x 3 full factorial
experiment.
Analysis of Variance Table
Source 
SS 
df 
MS 
F 
P 
A 
13,225 
p  1 = 1 
13,225 
9.45 
0.004 
B 
2450 
q  1 = 2 
1225 
0.88 
0.427 
AB 
9650 
(p1)(q1) = 2 
4825 
3.45 
0.045 
WG 
42,000 
pq(n  1) = 30 
1400 


Total 
67,325 
npq  1 = 35 



In this experiment, Factors A and B were fixed effects; so F ratios were computed with that in mind. There were two levels
of Factor A, so p equals two. And there were three levels of Factor B, so q equals three. And finally,
each treatment group had six subjects, so n equal six. The table shows critical outputs for
each main effect and for the AB interaction effect.
Many of the table entries are derived from the sum of squares (SS) and degrees of freedom (df), based on the following formulas:
MS_{A} = SS_{A} / df_{A} = 13,225/1 = 13,225
MS_{B} = SS_{B} / df_{B} = 2450/2 = 1225
MS_{AB} = SS_{AB} / df_{AB} = 9650/2 = 4825
MS_{WG} = MS_{WG} / df_{WG} = 42,000/30 = 1400
F_{A} = MS_{A} / MS_{WG} = 13,225/1400 = 9.45
F_{B} = MS_{B} / MS_{WG} = 2450/1400 = 0.88
F_{AB} = MS_{AB} / MS_{WG} = 9650/1400 = 3.45
where MS_{A} is mean square for Factor A, MS_{B} is mean square for Factor B,
MS_{AB} is mean square for the AB interaction,
MS_{WG} is the withingroups mean square,
F_{A} is the F ratio for Factor A,
F_{B} is the F ratio for Factor B, and
F_{AB} is the F ratio for the AB interaction.
An ANOVA table provides all the information an experimenter needs to (1) test hypotheses and (2) assess the magnitude of treatment effects.
Hypothesis Tests
The Pvalue (shown in the last column of the ANOVA table) is the probability that an F statistic would be more extreme (bigger) than the
F ratio shown in the table, assuming the null hypothesis is true. When a Pvalue for a main effect or an interaction effect is bigger
than the significance level, we accept the null hypothesis for the effect; when it is smaller, we reject the null hypothesis.
Source 
SS 
df 
MS 
F 
P 
A 
13,225 
p  1 = 1 
13,225 
9.45 
0.004 
B 
2450 
q  1 = 2 
1225 
0.88 
0.427 
AB 
9650 
(p1)(q1) = 2 
4825 
3.45 
0.045 
WG 
42,000 
pq(n  1) = 30 
1400 


Total 
67,325 
npq  1 = 35 



For example, based on the F ratios in the table above, we can draw the following conclusions:
 The Pvalue for Factor A is 0.004. Since the Pvalue is smaller than the significance level (0.05),
we reject the null hypothesis that Factor A has no effect on the dependent variable.
 The Pvalue for Factor B is 0.427. Since the Pvalue is bigger than the significance level (0.05),
we cannot reject the null hypothesis that Factor B has no effect on the dependent variable.
 The Pvalue for the AB interaction is 0.045. Since the Pvalue is smaller than the significance level (0.05),
we reject the null hypothesis of no significant interaction. That is, we conclude that the effect of each factor
varies, depending on the level of the other factor.
Magnitude of Effects
To assess the strength of a treatment effect, an experimenter can compute eta squared (η^{2}). The
computation is easy, using sum of squares entries from an ANOVA table in the formula below:
η^{2} = SS_{EFFECT} / SST
where SS_{EFFECT} is the sum of squares for the main or interaction effect being tested
and SST is the total sum of squares.
To illustrate how to this works, let's compute η^{2} for the main effects and the interaction
effect in the ANOVA table below:
Source 
SS 
df 
MS 
F 
P 
A 
100 
2 
50 
2.5 
0.09 
B 
180 
3 
60 
3 
0.04 
AB 
300 
6 
50 
2.5 
0.03 
WG 
960 
48 
20 


Total 
1540 
59 



Based on the table entries, here are the computations for eta squared (η^{2}):
η^{2}_{A} = SSA / SST = 100 / 1540 = 0.065
η^{2}_{B} = SSB / SST = 180 / 1540 = 0.117
η^{2}_{AB} = SSAB / SST = 300 / 1540 = 0.195
Conclusion: In this experiment, Factor A accounted for 6.5% of the variance in the dependent variable;
Factor B, 11.7% of the variance; and the interaction effect, 19.5% of the variance.