Once you have generated a hypothesis, the process of hypothesis testing becomes important.
For testing, you will be analyzing and comparing your results against the null hypothesis, so your research must be designed with this in mind. It is vitally important that the research you design produces results that will be analyzable using statistical tests.
Most scientists understand only the basic principles of statistics, and once you have these, modern computing technology gives a whole battery of software for hypothesis testing.
Designing your research only needs a basic understanding of the best practices for selecting samples, isolating testable variables and randomizing groups.
Hypothesis Testing Example
A common statistical method is to compare a population to the mean.
For example, you might have come up with a measurable hypothesis that children have a higher IQ if they eat oily fish for a period of time.
Your alternative hypothesis, H1 would be
“Children who eat oily fish for six months will show a higher IQ increase than children who have not.”
Therefore, your null hypothesis, H0 would be
“Children who eat oily fish for six months do not show a higher IQ increase than children who do not.”
In other words, with the experiment design, you will be measuring whether the IQ increase of children fed oily fish will deviate from the mean, assumed to be the normal condition.
H0 = No increase. The children will show no increase in mean intelligence.
From IQ testing of the control group, you find that the control group has a mean IQ of 100 before the experiment and 100 afterwards, or no increase. This is the mean against which the sample group will be tested.
The children fed fish show an increase from 100 to 106. This appears to be an increase, but here is where the statistics enters the hypothesis testing process. You need to test whether the increase is significant, or if experimental error and standard deviation could account for the difference.
Using an appropriate test, the researcher compares the two means, taking into account the increase, the number of data samples and the relative randomization of the groups. A result showing that the researcher can have confidence in the results allows rejection of the null hypothesis.
Remember, not rejecting the null is not the same as accepting it. It is only that this particular experiment showed that oily fish had no affect upon IQ. This principle lies at the very heart of hypothesis testing.
The vast majority of scientific research is ultimately tested by statistical methods, all giving a degree of confidence in the results.
For most disciplines, the researcher looks for a significance level of 0.05, signifying that there is only a 5% probability that the observed results and trends occurred by chance.
For some scientific disciplines, the required level is 0.01, only a 1% probability that the observed patterns occurred due to chance or error. Whatever the level, the significance level determines whether the null or alternative is rejected, a crucial part of hypothesis testing.
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