Although I'm a long way from my conservation biology days, I often get asked why science and scientists and never say that something is happening for sure or that something is causing something for sure? Doesn't science "prove" this or that? These are great questions and they usually come up over beer or a cocktail. So here is my having-a-beer-at-Munich-Oktoberfest response (which is actually one place these questions did come up).
Here's the T: science (and any knowledge system for that matter) can never prove anything, it can only disprove things. You can have a theory about something and gather more and more evidence that supports your theory and even get to the point in which you’ve gathered so much evidence that supports your theory that you might say that you're confident in your theory. But good science, and a good scientist, can never say that something is proven. You could say something like “we’re finding more evidence to support…” , “evidence continues to support…”, or even “evidence strongly supports…”. But even with overwhelming observations that support your theory, it cannot be said that anything is proven because you never know what tomorrow may bring.
For example, you might say “all crows are black”. Over time, you make observations that further support your theory, such as seeing more and more black crows over the course of your life. Then, on your 99th birthday you see an albino crow and your theory is now disproven…after all that time! At this point you could alter your theory and say “most crows are black” and still also be able to say that evidence “continues to support” or “strongly supports” your theory.
The fact that science can't prove anything can be frustrating for lots of folks, including policy makers, politicians, and probably your grumpy uncle who all want definitive answers on things. But once you understand how it all works, you become more comfortable with why science and scientists always use the cautious language around science "proving" things.
The next step in understanding how science and scientists can very comfortably say things like "there is strong evidence to support..." comes from probability and statistics. I offer an example below, but it might be more valuable to do an internet search for videos or visuals to understand probability and statistics, especially if you're a visual person.
Our Friends, Probability & Statistics
Using our crow example, we now know that all crows are not black...but we do think that most crows are black. We can use probability (the likelihood or possibility of something happening) to think about the likelihood of finding mostly black crows in our observations. The more observations that are made, the more those inform our probability calculations.
If we want to get even more formal about things, we can actually use statistics to test our theory. If we were to say that the if crow coloring were up to chance, 50% or less of crows observed would be black. The alternative to this would be that more than 50% of crows are black. Luckily, we don't have to count every crow in the world for this, we just need to get to what we (and math) tell us is a good representative population of crows to start testing our theory. If crow coloring (black vs. some other color) were actually up to chance, you'd expect that your observations would mostly land around that 50% of the population realm. Actual observations might fall a bit above or below this too since chance can also produce that outcome. For example, a coin toss should produce 50% heads and 50% tails, but in actuality, it can be a little off from that the more rounds of observations you do. This overall outcome of observations falling mostly around that 50% mark forms a bell-curve of outcomes with the peak of the bell being at the 50% line. So now you gather all your observations (in real life or perhaps using a model) and have many outcomes in which more than 50% of the crows observed were black. You'll likely be able to reject the theory that 50% or less of crows are black, which is great. Mathematics give us a tools, tests, and a there should for a certain level of observations occurring that allow us to say that the likelihood of chance producing the outcome we're observing is very unlikely. When our observations or at the ends of the observed or expected bell-curve, that's when scientists will say that their observations are statistically significant.
In terms of the coin toss example. Let's say you do 100 rounds of flipping the coin 10 times. How many outcomes of that coin coming up heads 85% of the time would make you think that something is indeed up with the coin?
Once you find a statistically significant result in your work, you might refine your theory and do more research and observations, or perhaps re-analyze your own research or the research of others. So, you could come up with the theory that 95% of crows are black. This is all good and you may indeed have statistically significant results in your ongoing research.
An Island of White Crows
But then, an island off the coast of Greenland is found were almost all crows are white! Does this mean your theories have been wrong? No. Does this mean all the time and effort (and perhaps money) you've invested has gone to waste? No. It just shows once again that science can't prove anything, it can only disprove things. It can add more and more evident to support things, but one never knows when we might learn something new about the world that makes us rethink our theories of things. It may make us expand our theories or allow them to be more flexible. Science (and any knowledge system) is still a fantastic and valuable tool, it's not the tool that many people think it is.