Cherry-picked data can be factual, and still imply conclusions that are not supported by other data. Excluding any data that is not supportive of ones view is that opposite of what a scientist should do. A scientists should actively seek out data contrary data and evaluate it. That's what I do. I come to places like this to get a sense of what contrary theories there are out there and evaluate those theories in light of other data. Sometimes it leads to a reconsideration of my own view, and sometimes I can reconcile that contrary data with what I have already seen. But to just ignore contrary data is the definition of cherry-picking.
This is good advice, but it also means not to exclude data that is contrary to what you believe. Also I would advise people to watch out for appeals to arrogance. For example, when you see a posting that cites an academic or technical paper from a professional journal, and then invites you to conclude what the poster wants you to conclude from it, consider the possibility that fully understanding that paper may be beyond your current training and expertise. When faced with the choice between agreeing with the poster's analysis of the technical article, or admitting that you are not qualified to understand it, the temptation is to give in to your arrogance and refuse to believe that you might not be qualified for understanding that paper. This is why such ploys are often successful. This is the basis behind the children's story, "The Emperor's New Clothes." In that story, the con artist appeals to the arrogance of the emperor and all his advisors to keep them from admitting that they just don't see what others are seemingly able to see. Finally it takes a small child with no pretense of superiority to defend to recognize and admit that the emperor does not have any clothes on. So don't be tricked by con artists who lull you into thinking you must be stupid if you cannot understand a technical journal. If you are not a professional in the field for which the journal was written, just admit to yourself that you may not have the specialized training needed to critically review the paper, and defer to those who do have that skill. That is why you seldom see misinformation based on publications made for the general population because it is easier to bamboozle people with faulty analyses of articles they cannot evaluate.