Approach and rationale to our statistical analysis

Data Collection
Overall, the data collected was good and there was no rejection.

Hypothesis Testing
1. Null Hypothesis: There is NO significant relationship between a person's oral temperature and axilla temperature.
2. Alternative Hypothesis: There IS a significant relationship between a person's oral temperature and axilla temperature.

Criteria to "Reject" or "Accept" the Null Hypothesis:
1. The decision rests on the p-value test statistic in relation to the user-defined α (alpha).

2. Alpha refers to the significance level. At this critical region, the range of values of the test statistic indicates that there IS a significant relationship and that the null hypothesis is rejected.

3. If the test statistic falls in the critical region (ie. p-value is ≤ α), it would lead to the rejection of the Null Hypothesis. In other words:

If p is ≤ α, we REJECT the Null Hypothesis.
If p is > α, we FAIL TO REJECT null hypothesis and we've to ACCEPT the Alternative Hypothesis.

In our study, we have set α at 0.05.

Choosing an appropriate statistical test

The statistical test depends whether the research question is about:

- Difference, or
- Correlation.

Our study is about correlation.

source: Chia, C. Y. (2008). Statistics in health sciences. (4th ed.). Singapore: McGraw Hill Education


Since both of our variables in question are scale variables, and guided by the above decision path, we use Pearson's r to do the testing.

What is Pearson's r?

1. Pearson's r let us know the strength and direction of the linear association between two scale variables.

2. This correlation coefficient indicates the strength of the correlation.

3. Its limit ranges from -1 to +1. The + or - values indicates the direction of the correlation and lets us know how the variables are related.

4. Values near -1 indicates a strong negative relationship and is visually represented by a downward slope of the samples in a graph, while values near +1 indicates a strong positive relationship and an upward slope of the graph.

5. The closer the correlation coefficient approaches zero, the weaker the relationship between the two variables.