When you conduct a study, you are testing a theory or a claim which you believe to be trus. For example;
Is there a difference between academic high achievers and academic low achievers in their attitudes towards smoking?
Is it true that arts students are more creative than science students?
Is the discovery method better than the inquiry method in enhancing performance in science?
A hypothesis is a statement about the relationship between two of more variables. Can you identify the variables in the two research questions above. Generally, researchers focus on two types of hypothesis:
Both the Null Hypothesis and Alternative Hypothesis describe two possible states of a phenomenon.
The Null Hypothesis (Ho) represents a theory that has been put forward because it is believed to be true. Say for example you conduct an experiment to test the effectiveness of the discovery method in learning science compared to the lecture method. You select a random sample of 30 students for the discovery method groups and 30 students for the lecture method group. Based on your sample you hypothesise that there are no differences in science achievement between students in the discovery method group and students in the lecture method group. In other words, you make the claim that there are no differences in science scores between the two groups in the population. This is represented by the following two types of notation and is called a Null Hypothesis or Ho:
Ho: µ1 = µ2 OR Ho: µ1 - µ2 = 0
The null hypothesis is often the reverse of what the researcher actually believes; it is put forward to allow the data to contradict it. In the above study, you expect science score for the two groups to be different.
For a null hypothesis to be accepted, the difference between the two means need not be equal to zero since sampling may account for the departure from zero. Thus, you can accept the null hypothesis even if the difference between the two means is not zero provided the difference is likely to be due to chance. However, if the difference between the two means appears too large to have been brought about by chance, you reject the null hypothesis and conclude that a real difference exists.
The Alternative Hypothesis (H1) is the opposite of the Null Hypothesis. For example, the alternative hypothesis for the study discussed earlier is that there is a difference in science scores between the discovery method group and the lecture method group represented by the following notation:
Ha: µ1 ≠ µ2
Ha: µ1 > µ2
Ha: The Alternative Hypothesis might be that the science scores between discovery
method group and lecture method group are different.
Ha: The Alternative Hypothesis might be that the science scores of the discovery
method group is higher than the lecture method group.
If the data are sufficiently strong to reject the null hypothesis, then the null hypothesis is rejected in favor of an alternative hypothesis. In other words, if the null hypothesis; µ1= µ2 is rejected then the alternative hypothesis would be µ1 ≠ µ2.
If we conclude "Do not reject Ho", this does not necessarily mean that the null hypothesis is true, it only suggests that there is not sufficient evidence against Ho in favour of H1. Rejecting the null hypothesis then, suggests that the alternative hypothesis may be true.
After having completed this topic you should be able to:
differentiate between a null and alternative hypothesis