How can I avoid being "the negative one" when giving feedback on statistics? Results get sent around a group of biological collaborators for feedback. Comments come back from the senior members of the group about the implications of the results, possible extensions, etc. I look at the results and I tend not to be as good at the "big picture" stuff (I'm a relatively junior member of the team), but I'm reasonably good with statistics (and that's my main role), so I look at the details. 

Sometimes I think to myself "I don't think those conclusions are remotely justified by the data".  How can I give honest feedback in a way that doesn't come across as overly negative? I can suggest alternative, more valid approaches, but if those give the answer "it's just noise", I'm pouring cold water over the whole thing.
I would suggest approaching your colleague in a humble and inquisitive way (especially since you're a junior member of the team). If you start the conversation with "your conclusions are wrong and here's why" you're going to set a combative tone for the rest of the meeting. There may be reasons that they interpreted the data the way they did that you're not aware of.

Instead try approaching the situation with something similar to "I looked at the data and came to this interpretation, can you explain your interpretation to me?" You're a researcher in your own right, so junior or not your opinion should be valued. But at least with this approach you indicate that you are open to the idea of being wrong, and hopefully that will start a constructive conversation where you can debate the merits of analysis type A over type B, etc.