Unfortunately I'm not talking about getting hold of a nice batch of fresh fruit. Instead, it's a particularly common dishonest tactic. It's also one that is flagrantly against the principles of doing science.
What it consists of is making a statement that is true only about a specific especially well-chosen circumstance, and then pretending that you've made a general statement about the system at hand. This is offensive to me as a scientist because in science we're trying to understand the system -- all of it. The cherry pickers abandon honesty for word games.
Suppose we're trying to understand the global mean surface air temperature. There are many other things we could try to understand, but this one is fairly often looked at. After we look a bit, we notice several things. One is that the temperature varies from year to year. As we look in to this further, we see that several things happen which affect the temperatures. This includes having a more active sun (warmer), having a recent major volcano erupt (cooler), having an El Nino (warmer) or La Nina (cooler). After doing our best to subtract out all those effects, we see that there is still some variation year to year. That's the 'free variability' (the scientist's way of saying 'stuff happens'). It turns out that there are also some contributions from anthropogenic aerosols (cooling), increased greenhouse gas levels (warming), and other human activity (depends).
As we try to make our honest understanding of this complex system (are there other things that affect global mean temperature? how much?) we also have to wonder about how much data we need to collect before we can tell the difference between that free variability and a trend caused by one source or another. Remember what happened when you tried my climate change detection experiment. Even with random numbers, you got runs of several consecutive 'years' of warming or cooling. Free variability does this to you. So if you're looking for trends or other systematic things, you need to look at a long enough period that the free variability can't lead you to a mistaken conclusion. Plus, of course, you have to make that allowance for all the things that you know happen and affect the variable (global mean temperature) you're interested in but are due to processes (solar variability, El Nino, volcanoes, ...) that you're not concerned about at the moment (greenhouse gas levels).
It can be very difficult to do this even when you're trying to do it all correctly. One of the first satellite sounding temperature analyses (Spencer and Christy, 1992 or 1993, if I remember rightly) showed a large cooling trend at the same time that all other data sets showed a warming. This was very puzzling. Not long after, however, Christy (same one) and McNider (1994 or so) showed that this was because the data record started near an anomalously warm period (strong El Nino in 1982-3) and ended near an anomalously cold period (after the eruption of Mount Pinatubo). It's anomalous because we're not (in looking for signs of whether human activity affects global mean temperature) concerned with El Nino and volcanoes. Once those two obvious anomalous events were taken out, the 'cooling' trend vanished. Science being a small world, I ran in to McNider not long after he'd published that paper and we talked about it among other things.
One thing you can look for, even with no particular knowledge, is whether the author (blogger, commenter, ...) is considering other factors that can be involved. Even easier, and the cherry-pick which prompts me here, is to see how they selected the time spans they used and the data sets that are used. In the satellite example above, for instance, it was straightforward -- the authors used all the satellite period they had data for. Fair enough.
Since 1998, though, there's been an industry that is careful to not use all the data they could. Indeed they're aggressive about ignoring data. You don't need to be a specialist to know that this doesn't square with honest understanding of a complex system. People who are seriously trying to understand climate are continually complaining about wanting more data. Throwing away good data is inconceivable to them. But in that industry, they're not concerned with honest understanding. They wish to arrive at a conclusion and if they pick the right starting year (1998) and data set (CRU rather than GISS, for instance), then they can get the answer (a cooling 'trend') that they want.
Now to get that, they have to choose only one or two years, both from recent history, as the time to start their 'analysis'. If they choose any of the 100+ years before 1998 that we have a surface temperature record for, their conclusion is gone. If they use GISS rather than CRU, their conclusion is gone.
Further, even choosing that one year as the start would not be enough to preserve their conclusion if they were honest enough to examine the other things we know affect the climate system -- that was a year with a strong El Nino (warming) and high solar activity (warming). Instead they ignore this (either dishonest or simply not doing their homework) and make various declarations against anthropogenic climate change.
With a couple questions, then, a legion of authors/sites can be pitched for being unreliable:
* Are they playing the 'global cooling since 1998' game?
* More generally, would their conclusions hold up if the start year were chosen differently?
* Are they assuming that only one thing affects global mean temperatures?
If the do the first or third, they're lying or not doing their homework. If they don't address the second, they're at least not doing their homework.
I've been aware of this particular cherry-pick for some years now, and the popularity of cherry-picking among anti-scientific groups even longer. So I'll let you do your own check of how many sites or sources within 15 minutes you can find that commit this error. Depending on your reading speed, you should make 5 easily, and 20 if you're a quicker reader and have a fast connection.