GST 1 is a module at Maynooth University which aims to improve research skills and employability. To gain 5 ECTS for this module you need to attend 6 sessions and produce a diary entry or set of notes for each one.
Quantitative Methods in the Social Sciences 2: Introduction to Hypothesis Testing
Hypothesis testing starts with a specific question and based on a given level it is accepted or rejected. Simply, an assumption is made, evidence collected, and, based on the sample data, is the initial assumption reasonable?
Steps for Hypothesis Testing
- Identify the research question.
- Determine the null and alternative hypotheses. H0 – the null hypothesis is always the status quo – there is no difference, change, x is not guilty.Ha – the alternative hypothesis is that there is a difference
- Decide the level of significance – 95% or a p-value of < 0.05
- Decide if need a one or two-tailed test
- Gather data with a view to proving H0 untrue
- Calculate the test statistic
- If there is no significant difference between the two parameters then accept H0. If there is a difference at a 95% significance level the observed differences are so great that they are unlikely to happen by chance, therefore reject H0.
Rejecting the null hypothesis is not proof as we don’t have the whole population, but the significance level suggest that this is not due to chance.
A one-tailed test is used if there is direction, if we are saying something is greater than, above, below X. A two-tailed test has no direction an indicates difference or change. In general a two-tailed test is used.
Type I and Type II Errors
Setting a significance value of 95% or higher reduces the chances of Type I (rejecting H0 when it is true) or Type II (accepting H0 when it is false) errors.
|Accept H0||Reject H0|
|H0 is True||Correct||Type I (𝛂)|
|H0 is False||Type II (𝛃)||Correct|