[R] Problem with 2-ways ANOVA interactions

[Frodo Jedi]

Dear All, 
I have a problem in understanding how the interactions of 2 ways ANOVA work, 
because I get conflicting results
from a t-test and an anova. For most of you my problem is very simple I am sure.

I need an help with an example, looking at one table I am analyzing. The table 
is in attachment
and can be imported in R by means of this command: 
scrd<- 
read.table('/Users/luca/Documents/Analisi_passi/Codice_R/Statistics_results_bump_hole_Audio_Haptic/tables_for_R/table_realism_wood.txt',
 header=TRUE, colClasse=c('numeric','factor','factor','numeric')) 


This table is the result of a simple experiment. Subjects where exposed to some 
stimuli and they where asked to evaluate the degree of realism
of the stimuli on a 7 point scale (i.e., data in column "response").
Each stimulus was presented in two conditions, "A" and "AH", where AH is the 
condition A plus another thing (let´s call it "H").

Now, what means exactly in my table the interaction stimulus:condition?

I think that if I do the analysis anova(response ~ stimulus*condition) I will 
get the comparison between 

the same stimulus in condition A and in condition AH. Am I wrong?

For instance the comparison of stimulus flat_550_W_realism presented in 
condition A with the same stimulus, flat_550_W_realism, 

presented in condition AH.

The problem is that if I do a t-test between the values of this stimulus in the 
A and AH condition I get significative difference,
while if I do the test with 2-ways ANOVA I don´t get any difference. 
How is this possible?

Here I put the results analysis


#Here the result of ANOVA:
> fit1<- lm(response ~ stimulus + condition + stimulus:condition, data=scrd) 
>#EQUIVALE A lm(response ~ stimulus*condition, data=scrd) 
>
> anova(fit1)
Analysis of Variance Table

Response: response
                    Df Sum Sq Mean Sq F value   Pr(>F)    
stimulus             6  15.05   2.509  1.1000   0.3647    
condition            1  36.51  36.515 16.0089 9.64e-05 ***
stimulus:condition   6   1.47   0.244  0.1071   0.9955    
Residuals          159 362.67   2.281                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 


#As you can see the p-value for stimulus:condition is high.


#Now I do the t-test with the same values of the table concerning the stimulus 
presented in A and AH conditions:

flat_550_W_realism                         =c(3,3,5,3,3,3,3,5,3,3,5,7,5,2,3) 
flat_550_W_realism_AH                   =c(7,4,5,3,6,5,3,5,5,7,2,7,5,   5) 

> t.test(flat_550_W_realism,flat_550_W_realism_AH, var.equal=TRUE) 

Two Sample t-test 

data:  flat_550_W_realism and flat_550_W_realism_AH 
t = -2.2361, df = 27, p-value = 0.03381 
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval: 
-2.29198603 -0.09849016 
sample estimates: 
mean of x mean of y 
3.733333  4.928571 


#Now we have a significative difference between these two stimuli (p-value = 
0.03381) 



Why I get this beheaviour?


Moreover, how by means of ANOVA I could track the  significative differences 
between the stimuli presented in A and AH  condition
whitout doing the t-test? 

Please help!

Thanks in advance


      
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