research report interpreting data gathered through testing hypothesis

report on interpreting data gathered through testing hypothesis

1. INTERPRETING DATA GATHERED THROUGH TESTING HYPOTHESIS RHYSLYN T. RUFIN Discussant EDF 303- Quantitative Methods of Research

2. Educational Research deals with two kinds of statistical data, descriptive data and statistical data (Mathews & Ross,2010).

3. Statistical Analysis is a series of techniques in presenting the findings for analysis and interpretation. This was done to explore and address the research questions that have been posed for interpretation.

4. What is a Hypothesis?

5. A hypothesis is a prediction or guess of the relation that exists among variables being investigated (Wonnacott&Wonnacott,1990 ) . A hypothesis must be stated so that it is capable of being either refuted or confirmed. The result will answer relationships that exist among variables.

6. In the previous cited example, on students who would frequent visit the library, a perception may be formed. As study wanted to find out whether, the students who are “frequent library users” or “ seldom library users” will differ their academic performances.

7. According to Wonnacott (1990), we usually settle this argument by constructing a 95% confidence interval . In general, any hypothesis that lies outside the confidence may be judged implausible, that is, it can be rejected.

8. On the other hand, any hypothesis that lies within the confidence interval maybe judged plausible or acceptable. In conforming to the tradition, we usually speak of testing at an error of 5%.

9. The hypothesis, according to the author ( Wonnacoot & Wonnacott , 1990), is of particular interest, it is called null hypothesis since it represents no difference whatsoever. In rejecting it because it lies outside the confidence level, we establish the important claim that there was indeed a difference between students who are “frequent users”. The result is traditionally called statistically significant at 5% significance level.

10. There is problem with the term “statistically significant”. It is a technical phrase that simply means enough data has been collected to establish that differences do exist. It does not mean that the difference is necessarily important. Wonnacott and associate went on to explain that,

11. Statistically significant at 5% significance level is the traditional phrase typically encountered in the scientific literature. It means exactly the same things as our statistically discernible at 5% error level.

12. If a 5% level of significance is being used, it would be natural to speak of the hypothesis being tested at a 5% confidence level. Now, to return to our example, let us formally conclude that 5% level of significance, we can reject the hypothesis of no difference.

13. In other words, we have collected enough evidence so that we can see a difference in academic performance between “frequent library users” and “ seldom library users”. This means that the result is statistically different.

14. In print data of commonly used statistical package, the decision criteria for accepting or rejecting hypothesis is on the computed p-value (significance level). The p-value summarizes clearly how much agreement there is between the data or null hypothesis ( Ho ). The p-value is an excellent way to summarize what data says about the credibility of Ho.

15. The statistical test is used to determine whether or not a hypothesis is correct by telling the researcher how likely it is that the results of an experiment are due to chance alone. Generally, a null hypothesis is set up stating that there is no difference between the control and experimental samples. The data are than collected and analyzed , and the null hypothesis is either accepted or rejected.

16. Thank you!