How can I combine data from 2 separate experiments? I am a scientist and by no means a statistician. I have, I think, a very basic question for you all. 
I have performed the same experiment twice (analyzing levels of a certain protein) and am comparing levels in untreated vs treated animals. I performed a t-test with welch correction (because there was significant variance). I am observing a similar trend, but neither experiment alone reaches statistical significance. Is there a way to combine the two experiments to increase power? Although the trend is similar, the actual values are different between the experiments, which is, I think, increasing my standard error. Also, if anybody knows how to perform this analysis using graphpad prism, that would be ideal.
Thanks so much in advance for any assistance you may offer.  Please let me know if you need more information.    
 A: Effects should be different across experiments, and so should variances. That's the nature of sampling. What you have is just different samples being different. There's no way to know which estimate of variance is closer to true value, or even guess at it with the information you've given and equal N's in the samples. So, while you'd like it to be the smaller one, that may not be correct. More than likely the average variance is best.
Your general tactic here of searching for an effect may eventually bare fruit. You may combine all of the subjects into one experiment, run a few more, drop an outlier here or there, look for various analysis techniques, and voila, a significant effect. Maybe you won't do all of that but I'm trying to point out that you're thinking about it wrong. Use the data you have to make your best determination about the truth of the matter, not show an effect.
An important thing to keep in mind is that an unstated assumption about any statistical test is that you're performing it because you want to know the answer to the test, not because you've previously done other tests and failed to find what you would like to find. So now, because you've already done the test, the rate of Type I error is no longer what you set it to be, alpha. You're increasing the probability of finding an effect whether there is one or not.
That said, you could do something that's not a test. You could construct a confidence interval of the effect through a mega-analysis (just combine all of the data) and report that as a higher quality estimate of the effect than either experiment had alone. You will have to concede that what you've done is post hoc and describe the tests that you did do already. But this is probably the best way to report what you've done so far.
