As we are able to choose any topic relevant to research methods.. I thought I’d go for something different.

Another week, another blog. The topic up for debate this week is “Is it possible to prove a research hypothesis”. Proof is simply defined as sufficient evidence to show something as true. A hypothesis is a supposed explanation for a phenomenon which can be tested. So now that the key terms are defined lets get down to the matter at hand.** **Is it possible to prove a research hypothesis? The answer… no, we cannot prove any of our hypotheses in an observational science and here is why.

In observational sciences we gather data through inductive reasoning, which is making observations and using them to make generalisations. So an example may be that, every old woman I have ever met has been called Doris or Ethel(observation), therefore I can assume there’s a good chance that the next old woman I meet will be called Doris or Ethel(hypothesis). We could then perform countless studies in which we asked old women their name, and every time our results could support my hypothesis. However just because this is the case for the people we have studied doesn’t mean we can assume it’s true for all other old women. “ No amount of experimentation can ever prove me right; a single experiment can prove me wrong” – Albert Einstein. David Hume takes a similar stance in staying “we are never justified in reasoning which is from repeated instances.”**.**

A common counter-argument which follows is that each observation that supports our hypothesis brings us a little closer to proving it as we’ve eliminated one more possible uncertainty from the list. Unfortunately not, the following example despite overused demonstrates why this is the case beautifully. If you successfully start your car each morning does that mean that is more likely to successfully start tomorrow? With each time that you successfully start your car, does this increase the possibility that it will continue to start at every attempt for the rest of time? Of course not, so as this example has shown, we cannot generalise with certainty from previous observations.

Although we’re unable to prove a theory we are able to disprove as shown in Einstein’s quote e.g. We could disprove that not all ravens are black, with the observation of a single white raven. So theoretically if you were to disprove all antitheses(hypothesis which contradicts our hypothesis) you could prove a hypothesis. However in reality identifying all anti-thesis is impossible. To conclude I feel it’s important to point out that despite being unable to prove theories, we can be very confident of a theory. Which in science seems to be the next nearest thing. A great example is gravity, despite being unproven it is a theory that almost all physicists would accept to be true, to the extent that developed theories since often rely on it being true. So to summarise; is impossible in observational sciences as they are based on inductive logic.

I like that you’ve gone your own way with this week’s blog topic. I guess it might be possible to prove a research hypothesis if there were a finite number of things to observe, but I guess then you wouldn’t be able to prove that there were a finite number of things now i think about it. You’d have to disprove every anti-thesis in the world and then be sure that every anti-thesis had been disproved. I guess that’s the flaw with inductive logic though. Experimentation and deductive logic definitely seem better in sciences than observational and inductive. Nice blog, definitely got me thinking!

This blog I like very MUCH! I liked your conclution : “So theoretically if you were to disprove all antitheses(hypothesis which contradicts our hypothesis) you could prove a hypothesis”, well defenetly “unstopping train” of experiment hypothesis and falsifications. Nowdays people looking forward to dissprove everything, it became like: I DON’T TRUST ANYONE! However, change the format of writing ( too small, really, can’t see) and another question, you deffenetly need hard work sometimes to disproove the hypothesis, and in the head there is allways “middle position” between what is true and false.

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I enjoyed how you presented your view in the sense we can never actually disapprove our research hypothesis in a sense, it was a refreshing defeatist view. Although maybe a counter argument to how even though it may seem futile aren’t there any benefits to such a view or common ground because of it.

You give the definition yourself that that proof is sufficient evidence to show something as true, so how much evidence is sufficient? Its impossible to prove a null hypothesis but if you have numerous papers and studies that indicate a finding to be true is that not proof. Proof can be showing that you are not wrong it is not necessarily that you are right. Brian cox outlines this at 2.35 in this audio clip.

http://news.bbc.co.uk/today/hi/today/newsid_9622000/9622751.stm