Compare/Interpret t-statistics I've a table as the following that contains the results of one sample t-test on four independent collections for the null hypothesis that the mean is 0:
Collection  t-Statistic p-value     Cohen's d   Interpretation
A           8.17        2.31E-13    0.40        Medium effect size
B           9.30        9.94E-14    0.30        Small effect size
C           2.17        7.99E-06    0.18        Small effect size
D           18.23       6.78E-89    0.21        Small effect size

According to the p-value and t-statistic, I can reject the null hypothesis for all the four collections. However, the question I am trying to answer is, which one is more significant than the other one? or, can I make such inferences on this data? 
For instance, can I say mean is more significantly different than 0 in collection B than in collection C based on the p-value and t-statistic. And, the mean in collection A is more significantly different from 0 than in collection D because Cohen's d in collection A has medium effect size while in collection D with it has small effect size. Does making such inferences make sense (or are accurate)?
 A: In trying to compare two population means by looking at
the difference in sample means, there may be three criteria
to consider.
1) Practical importance. If you're wondering whether a method
of helping people lose weight is effective, you might have a huge number
of subjects. Then you might get a very small P-value, and so a "highly significant" difference. But if the actual average weight loss of a group of subjects who used the method for a month was 1/2 lb (or 1/4 kg), you might decide that even though
the effect of the method may be significant, it is too small
to be of practical importance. Whether a real difference is of practical importance is a judgment to be made by people who
are familiar with the measurements and with the practical effect of a change.
2) Statistical significance. Statistical significance is often judged by looking at the P-value of the test. It is the probability of a result more extremely different from the null hypothesis. If this probability is very small (say, below 5% or 1%)
you can say that the departure from the null hypothesis is 'statistically significant' (at the chosen level). In the
weight loss example, you might have a small group of subjects
with an average weight loss of 22 lbs (10 kg). If real, that
amount of weight loss might be of practical importance. But
for a small group of people, the significance level might not
show a significant decrease in weight. Then you might have
anecdotal evidence that the method worked well for a few people, but not enough evidence to be generally convincing to people
who care about statistical significance. 
3) Measures of effect size. Cohen's $d$ is one of a number
of proposed measures of effect size. Roughly speaking it measures the difference obtained by looking at a difference between two means in terms of the number of standard deviations that difference represents. It is often used in two-sample tests, so that $d = \frac{\bar X_{\mathrm{trt}}-\bar X_{\mathrm{ctr;}}}{S},$
where $s$ is an estimate of the standard deviation based on the two samples. 
In (1) "practical importance" may be judged by
experts on the kind of data involved. Cohen's $d$ can be used
as a fixed standard of what practical importance may mean. If the observed difference between treatment and control groups is half a standard deviation $(d \approx 1/2),$ then the effect may be interpreted as moderate in size; if $ d \approx 1,$ then the effect may be interpreted as large or very large. 
In a weight loss study, subjects who are carefully screened and about equally motivated may yield very similar weight losses, giving a small $s$ and inflating $d.$ Then people who know and care about typical weight losses may have differences of opinion about the usefulness of Cohen's $d.$
Consider a two-sample t test on (simulated) data
for $n_1 = n_2 = 500$ treatment and control subjects
with weight losses x.trt and x.ctrl.
        Welch Two Sample t-test

data:  x.trt and x.ctrl
t = 24.559, df = 995.12, p-value < 2.2e-16
alternative hypothesis: 
   true difference in means is not equal to 0
95 percent confidence interval:
 4.446439 5.218732
sample estimates:
mean of x mean of y 
 14.83626  10.00367

Here the observed difference in sample means is about 4.83 lbs., and Cohen's $d$ is about 1.5.
The difference is very highly significant, and Cohen's $d$ is large, but people who know and care about weight
loss may argue whether a difference in weight loss of
less than 5 lbs. is of practical importance.
