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The null hypothesis has haunted my scientific investigations since Secondary school with no one ever really properly describing what it is or what is its purpose. When I was first introduced to the concept I was told that it was the opposite of the experimental hypothesis. So for this blog I am going to try and better understand what the null hypothesis is and what part it plays in research.

The Research methods textbook from last year described the null hypothesis as the “statement about the population or treatments being studied that says there is no change, no effect, no difference or no relationship.” The null states that there would be no change whereas the hypothesis aims to describe “a tentative description or explanation for the relationship between variables”. So in general terms the null hypothesis is the opposite of the experimental hypothesis. Where the hypothesis says that something will happen while the null hypothesis says nothing will.

Ok, that seems simple enough; one says that something will happen whereas one says that nothing will. However what is the point of having two hypotheses for the same theory?

Well one of the uses of the null hypothesis is that it helps to define what exactly the population would look like if nothing happened. So for example if you were doing research on the difference in mortality rates between those who when admitted to hospital suffering with depression; and were treated with anti-depressants, electroconvulsive therapy or neither (Avery, David and Winokur, George in 1976 Mortality in Depressed Patients Treated with Electroconvulsive Therapy and Antidepressants). So in this case the null hypothesis would state that there would be no difference between the three treatment conditions. There wouldn’t be any significant differences between the mortality rates for those who were treated with the electroconvulsive therapy, antidepressants or nothing.

The other advantage of using a null hypothesis is that it is much easier to completely reject than it is the reject the experimental hypothesis. This is because there is only one way the null hypothesis can be satisfied and that is if there is no difference. Whereas the experimental hypothesis is much more open and therefore could be satisfied through a number of ways. Using the previous example of the study on levels of mortality, the experimental hypothesis could be satisfied if those who received the antidepressants had a lower mortality rate than the other two treatment conditions, or if it had a higher mortality rate. Another way could be if the treatments of electroconvulsive therapy and antidepressant had a much higher mortality rate than the condition of not receiving either; and so on and so forth. So the null hypothesis sort of acts a bit like a shortcut, as it reduces the amount of testing that would be required to test all of the potential hypotheses that could be incorporated into the experimental hypothesis. In other words if there is any significant difference between the groups, the researcher can reject the null hypothesis straight of the bat; whereas if there is no difference between the treatment conditions, the experimental hypothesis can be rejected and the null accepted.

While the null hypothesis can seem as unnecessary and superfluous, it actually makes life and research much easier to understand and it helps to reduce the amount of time needed to form conclusions on pieces of research.

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Reliability can be defined as the amount of consistency in the measurements (Research methods for the behavioural sciences, by Gravetter and Forzano); in other words if the same individuals were measured under the same conditions, the same measurements should be recorded if the procedure is reliable. It is a vital section of the scientific world especially in research. However it is also important in day to day life, like it is nice to know that when you get in your car you will get to where you want to go and not break down in the middle of nowhere. So in my opinion reliability is a very important consideration for the creation of the research design, and the purchase of a car.

Reliability is one of the main factors that comes into question when papers and research are being peer reviewed by other members of the scientific community whether they are psychology based or not. This is because if the research done is reliable it means that other researchers can recreate the studies and they should be able to get similar effects or results to the original paper, even though they are doing the study with different people. This process allows for research to be critically analysed therefore increasing the accuracy and the chance for the conclusion formed to be correct. The ability to recreate methods effectively and accurately may also be important for further studies that are using it as a base or starting point. For example the study done by Prokosch, Yeo, and Miller on how intelligence and general fitness may have a correlation (Intelligence tests with higher g-loadings show higher correlations with body symmetry: Evidence for a general fitness factor mediated by developmental stability) would not have possible if there have not been a reliable method for calculating intelligence. If there had been no reliable method for calculating intelligence then they would not have been able to calculate the intelligence and therefore could not have formed their conclusion.

Reliability however is based on the assumption that the variable being measured or observed is constant. This assumption however is impractical and often difficult to fulfil in reality and real world situations as it is often impossible to recreate exactly the same conditions, and even if you do manage often there will be some variability in the results or data. By the very nature of the world things change, your nice new, very reliable car will probably one day break down and leave you stranded because it is no longer as new as it was when you first got it. In the psychological world one example of this would be intelligence; this is considered to be a fairly constant variable in a person. However if you had the same individual do the same intelligence test they would probably not get the same score each time. This could be due to a variety of reasons; one day they might be ill, or tired (or hung-over) and as such would not get the same score as they did previously which would therefore make the test unreliable.

Although it is often impossible to make a method completely reliable, due to human nature and the fact that very little is completely constant with absolutely no fluctuation; having a method that is reliable does help in the procuring of data as well as analysing it. If a method has been well designed and the researcher can trust it is reliable then they can concentrate on just collecting the observations or measurements and just follow the directions as they are set out in the method. This is especially useful for psychologists who may be doing studies with vulnerable or delicate participants or situations; for example children.

Despite the pitfalls that can accompany reliability and whether or not a research design or operational definition is reliable, it is a necessary consideration that must be taken into account when doing designing research procedures. Without reliability in the scientific community we would be unable to peer review papers, use other research as bases for new ideas and collect enough data in order to back up the theory we are investigating.

Statistics can be a useful tool for analysing and interpreting data, however they are not a necessity because there are other ways that can be used to understand the information.

Graphs, such as histograms and bar charts can help to graphically provide information about the relationships within the data. They also allow for descriptive statistics to be very quickly determined, without many calculations needing to be done especially in the cases of the modes, and medians of the data. Whereas in the case of line and scatter graphs they can show correlations both positive and negative ones by the gradient of the lines or scatter.  This means that you can very quickly find out the relationship between the data, it also does not require people to have previous statistical knowledge, which therefore makes the data more accessible for the public.

Although graphs can help you to identify relationships they do not tell you if the relationship between the two factors is significant or not. In order to find the significance you would need to do some kind of statistical calculation such as finding out the p-value of the data. Graphs and charts also can be misleading, for example a bar chart can be misleading because it doesn’t tell you the number of people who took part in the research and so you can form conclusions that are incorrect.

Observational research also does not always necessitate the use of statistics. In fact often if you were to try and quantify the observations you could actually lose some of the observations and what the observations could potentially tell us.

Although statistics can add further information to data and help to check the significance they are not necessary because data can be understood without them. In fact it is often easier to understand data that has been displayed in a graph, than trying to interpret a selection of numbers that you can make neither head nor tails of.