Education

1. Republic of the Philippines
PANGASINAN STATE UNIVERSITY
SCHOOL OF ADVANCED STUDIES
Urdaneta City
Telefax No. : (075) 568-2568
Course Code: EDF 302
Course Title: Quantitative Methods of Research
Topic: INTERPRETING DATA GATHERED THROUGH
TESTING HYPOTHESIS
Professor: Dr. Rosanna D. Gonzales
Discussant: Rhyslyn T. Rufin
I. Topic: INTERPRETING DATA GATHERED THROUGH
TESTING HYPOTHESIS
II. Introduction:
In every research, thesis or study, it is very important that the researcher/s are
familiar with the basic procedures in interpreting the research findings in order to shed
light and meanings to the results.
In this discussion, interpreting data gathered through testing hypothesis will be
explained in-depth for better understanding and application.
III. Discussion
Educational Research deals with two kinds of statistical data, descriptive data
and statistical data (Mathews & Ross,2010).
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.
In the interpretative sense of a study, there are readers who wanted to find and
understand the relationship of variables investigated so to present the expectations or
predictions of relationship between variables in the study.
-What is a Hypothesis?
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.
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.
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.

2. 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%.
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.
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,
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.
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.
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.
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.
IV. Conclusion
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.
V. References
Almeida,A. et.al. 2016, Research Fundamentals from Concept to Output, A guide for
researchers and thesis writers; Adriana Publishing, CO, Inc. Cubao,QC,
Manila,Philippines