“… the null hypothesis is never proved or established, but is possibly disproved, in the course of experimentation. Every experiment may be said to exist only to give the facts a chance of disproving the null hypothesis.” – R. A. Fisher
The null hypothesis takes a central role in the scientific process because it is impossible to reject or accept the experimental hypothesis. This is because there are an infinite number or variables that can have an effect on the difference caused between conditions, whereas the null hypothesis is simple.
Firstly, the null hypothesis states that there will be no differences between the conditions, or treatments. In other words, the samples being tested are from the same population and any differences that are found are very likely to be due to chance. The experimental hypothesis states that there will be a difference between conditions or treatment groups; this can be two-tailed (the direction of the difference is not stated) or one-tailed (the direction of the difference is stated, i.e. there will be an increase or decrease). For example, a researcher is studying the effects of smiling on dating behaviour. The null hypothesis would state that there is no difference between the amount of times a person smiles and the number of dates they go on. A one-tailed experimental hypothesis would state that there would be a difference between the amount of times a person smiles and the number or dates they go on, and the two-tailed could state that the more a person smiles the more number of dates they go on.
However, it is worth noting that some do argue that the null hypothesis is of no importance in the scientific world, such as Savage – “Null hypotheses of no difference are usually known to be false before the data are collected…when they are, their rejection or acceptance simply reflects the size of the sample and the power of the test, and this is not a contribution to science.”
To some extent this argument is true, as I mentioned in my previous blog entry a very large sample can make a small effect appear significant, when it is not, and research papers are normally not published if p = >.05 (the results do not show significance). Notwithstanding, the null hypothesis is still an important component of hypothesis testing, this is a method used in statistics where sample data is used to evaluate a hypothesis and make inferences about a population. Before selecting a sample the hypotheses (null and experimental) predict the characteristics that a sample should have. Once a random sample of the population, the researcher is interested in, has been gathered the obtained sample is compared with the prediction made in the hypothesis. There are 4 main steps to hypothesis testing:
- State the hypotheses (null and experimental)
- Chose an Alpha Level, this sets the criteria for making a decision about whether or not the null hypothesis is supported or should be rejected.
- Collect the data and compute the relevant sample statistics.
- Finally the researcher needs to make a decision about whether to reject the null, this happens when the sample is unlikely to occur if the null is true. So if the alpha level was set at .05 and the results showed p= <.05 this would be that there is a less than 5% chance of the null hypothesis being true therefore the null would be rejected. Or the researcher can fail to reject the null, this would be when the results show p= >.05 therefore falling out of the critical region and supporting the null. (After all the null hypothesis can never be proven because statistical tests deal with probability.)
Therefore the null hypothesis is crucial in the scientific process because it sets concrete boundaries allowing the researcher to know whether, or not, their results are significant or likely to have occured by chance.
Further reading of the null hypothesis can be found by visiting – http://www.null-hypothesis.co.uk/science//item/what_is_a_null_hypothesis
For me, not having a null hypothesis in my research would be like only having half a research question. It would go from being ‘Do anti-depressants have an effect on mood levels?’ to ‘Do anti-depressants exist?’ as I feel you’re removing the possibility to say no to the initial question. Without the null hypothesis, we only have one way (or two ways if the H1 is directional) to go and nothing to fall back on if we lose our way. It also serves as a great way of testing our theories out throughout time.
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I agree with you, but not only that with out the hypothesis how can you get any closer to rejecting theories and developing new ones. hypothesis are important in psychology as they allow researchers to develop new idea and form new concepts. with out them how would they know what reacts with what and the relationships between variables. it is like having a novel without the main story line, with out that then how can you develop ideas and grow from it?
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hi. i like your blog
i found this interesting point too… Accepting the null hypothesis does not mean that it is true. It is still a hypothesis, and must conform to the principle of falsifiability, in the same way that rejecting the null does not prove the alternative.
Many reasearchers see accepting the null hypothesis as a failure of the exeperiment, but accepting or rejecting any hypothesis is a positive result…
Read more: http://www.experiment-resources.com/null-hypothesis.html#ixzz1g3n4mayH
http://www.experiment-resources.com/null-hypothesis.html
Also i thought your blog went into great detail about the null hypothesis and give a good conclusion to why the null hypothesis is crucial to the scientific process 🙂
Yes, accepting the null hypothesis does not necessarily mean that there is no difference, not only because different samples of the same population will always produce a slight difference of means but because you are dealing with probabilities. If p>.05 you are saying it’s highly likely that null is correct, if p<.05 you are saying it's highly likely that the null is not correct, there is no definitive yes or no answer.
Hi I enjoyed your blog, and I felt that you provided a really balanced argument for both sides. However when you were talking about why the null hypothesis is useful you could have mentioned that it helps speed up the research. For instance the null hypothesis has only one possible outcome if it is correct, and that is there is no difference between the two samples. Whereas for the hypothesis there is a huge variety and would be nearly impossible to test for all of the possible outcomes. So the null helps to make rejecting or accepting a theory much quicker.
But overall I thought you had a good conclusion and gave a lot of detail. Hope you have a nice holiday 🙂
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