Links to my comments:

Evaluate the usefulness of Qualitative research methods. Qualitative methods such as case studies and interviews certainly have their place in science, especially in the social sciences. However qualitative research possesses a number of key flaws, and as a result is used less than quantitative methods. Shuval claim that qualitative research made up a mere 4.1% of research published in medical journals in 2007.

One important use of qualitative research is in the formulation of hypotheses. Case studies such as those of rare cases can have a large impact in that they galvanize the scientific community. A classic example of this is The Case study of H.M. For those you not familiar with this case I’ll summarise somewhat. H.M was a patient who underwent brain surgery, in which his hippocampi was removed in an attempt to cure his severe epilepsy. This resulted in devastating affect on his ability to form new memories. As it would be unethical to purposefully cause perform lesions such as this for research purposes, cases such as this are invaluable to our understanding.

However qualitative research such as this lack generalisability, as a result they may be used in developing hypotheses but lack value in themselves as studies(Dogan & Pelassy,1990). For example decisions about which treatment the NHS should use for Schizophrenia could not be based case studies. Although case studies of schizophrenia could be used to enrich our understanding of schizophrenia, and as a result aid in the development treatments. However before implementing such treatments quantitative research is required to assess it’s usefulness in the wider population.

Another key problem with using qualitative methods is the lack of control of variables, which is crucial for ensuring validity of results. Some would even go as far to say that due to their lack of control qualitative methods are scientifically valueless(Campbell & Stanley, 1966). This isn’t entirely the case, as mentioned above such methods can help with the formation of hypotheses and serve as a basis to build around. Nevertheless the lack of validity and generalisability makes it is difficult to develop knowledge. Considering that the aim of science explain phenomena in the world around us, means that qualitative research will always have limited use in science.


Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research. Chicago: Rand McNally

Shuval claim that qualitative research made up a mere 4.1% of research in 2007.

Dogan, M., & Pelassy, D. (1990). How to compare nations: Strategies in comparative politics (2nd ed.)

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. 

The question up for debate this week is whether having an understanding of statistics is beneficial. The answer…Yes and this doesn’t only apply to those looking to go into careers which require statistics, but to the laymen too. Having an understanding of statistics enables us suitably evaluate the myriad of numbers which bombard us daily, and thus guard yourself from being fooled or manipulated by them. So let’s see an example of a statistic I saw recently; “100% of people saw improvement in just 1 days! After 4 weeks, they had clearer skin and after 8 weeks it stayed clearer”. So to most people this product would seem like a great choice, if 100% of those tested showed an improvement and continued to show improvement it’s certainly going to work for you and I right? Not quite, a quick glimpse at the small print gives us a better idea at what is really being said here. The statistic is actually composed of two separate tests, in which two separate products within the kit are tested on two separate groups. However, the two statistics are slyly linked with the vague use of the word “they” to imply it’s one group. This infers is that everyone who used the kit experienced improvement, and continued to do so for 8 weeks. Whereas the reality is that a small group of 30 people found that the isolated use of 1 of the 3 products from the kit lead to an improvement. Another separate group of 31 people who trailed a different product within the kit found a improvement over 8 weeks. Therefore this statistic is pretty useless at telling us how effective the kit is as a whole, when all components are used in combination the way in which customers are directed to use it. As you can see from this example, a very minimal use of statistics understanding has shown us that the reality of this particular statistic is far from what it claims. In conclusion having a basic understanding of statistics can enable us to dissect figures given to us and determine their true merit, this overall enables us to make informed decisions in our everyday lives.

The statistic and small print mentioned above can be found at the following link: