TL;DR: A research hypothesis is a testable statement about a relationship or a difference, written before you collect data. Philippine manuals normally require it in the null form ("there is no significant relationship..."), with the alternative stated alongside. Not every study needs one: DepEd's own curriculum guide says "lists research hypotheses (if appropriate)," and purely descriptive or qualitative studies have nothing to test. Every hypothesis should trace back to one numbered sub-problem and forward to one named statistical test, with the alpha level fixed in advance.
If you are searching for how to write a research hypothesis, the usual sticking point is not the wording. It is that the hypothesis has been written in isolation, disconnected from the sub-problems above it and the statistical test below it, and a panel will pull on exactly that thread.
This guide covers what a hypothesis has to do, the null and alternative forms, when directional phrasing is defensible, and four worked illustrative sets that run the whole chain: sub-problem, hypothesis, test, decision rule, and correctly worded conclusion.
Which subject is this for? Practical Research 1 and 2 are applied subjects of the 2016 K to 12 Senior High School curriculum, and that structure is being replaced. DepEd Order No. 017, s. 2026 institutionalises the Strengthened Senior High School Curriculum for Grade 11 from SY 2026-2027 and Grade 12 from SY 2027-2028, and its Annex A lists Research 1 and Research 2 as 80-hour Academic Track electives in the Field Experience cluster rather than applied subjects everyone takes. Grade 12 learners not enrolled in pilot schools continue under the 2016 curriculum for SY 2026-2027. Hypothesis testing works the same way under either. See our guide to the Strengthened SHS Curriculum.
What a Hypothesis Is, and When You Need None
A hypothesis is a statement that a statistical test can contradict. That single criterion filters out most bad drafts. "This study will explore the effects of screen time on students" is a purpose statement. "There is no significant relationship between daily screen time and sleep quality" is a hypothesis, because a correlation test can produce evidence against it.
DepEd's Practical Research 2 curriculum guide is precise about when one is required. The competency reads "lists research hypotheses (if appropriate)" (CS_RS12-If-j-8), sitting in the same content block as the conceptual framework and the definition of terms. The parenthesis is the important part.
| Type of study | Hypothesis needed? | Why |
|---|---|---|
| Descriptive (what is the level, extent, profile) | No | Nothing is being tested, only described |
| Correlational (is there a relationship) | Yes | A relationship claim is testable |
| Comparative (do groups differ) | Yes | A difference claim is testable |
| Quasi-experimental or experimental | Yes | An effect claim is testable |
| Qualitative (phenomenological, case study, grounded theory) | No | The design explores meaning rather than testing a claim |
Most Philippine student theses are mixed: a few descriptive sub-problems followed by one or two relational ones. In that case you write hypotheses only for the relational sub-problems, and you say so. Writing a hypothesis for "what is the profile of the respondents" signals that the logic of testing was not understood.
Null and Alternative, and Why the Null Comes First
The null hypothesis (Ho) states that there is no significant relationship or no significant difference. The alternative hypothesis (Ha) states that there is one.
The null looks like the boring version, and students often ask why it is the one written down. The reason is mechanical: statistical tests compute how compatible your data are with a specific stated model, and "no relationship" is a specific model that can be computed. "There is some relationship of some size" is not. So the test evaluates the null, and evidence against the null becomes support for the alternative.
Most Philippine research manuals require hypotheses to be presented in the null form for exactly this reason. Some require both stated explicitly. Check your manual and follow it.
Worked Example 1: A Correlational Hypothesis
The study below is illustrative, constructed for this guide. It is not real research, and the figures used to demonstrate the decision are invented.
Illustrative study: the relationship between daily smartphone screen time and self-reported sleep quality among Grade 11 students in one public senior high school in Cebu City.
Sub-problem 4. Is there a significant relationship between daily smartphone screen time and self-reported sleep quality?
Ho. There is no significant relationship between daily smartphone screen time and self-reported sleep quality among the respondents.
Ha. There is a significant relationship between daily smartphone screen time and self-reported sleep quality among the respondents.
Test. Pearson product-moment correlation, since both variables are measured on continuous or near-continuous scales.
Level of significance. 0.05, fixed before data collection.
Decision rule. Reject Ho if the computed p-value is less than 0.05; otherwise fail to reject Ho.
Notice how tightly each line binds to the next. The sub-problem names the two variables; the hypothesis names the same two; the test is the one that fits their measurement level; the decision rule is stated before any data exists. A panel can follow that chain in ten seconds, which is the point.
Worked Example 2: A Comparative Hypothesis
Illustrative, same constructed study.
Sub-problem 5. Is there a significant difference in self-reported sleep quality between students in the STEM and TVL strands?
Ho. There is no significant difference in self-reported sleep quality between students in the STEM and TVL strands.
Ha. There is a significant difference in self-reported sleep quality between students in the STEM and TVL strands.
Test. Independent-samples t-test, two groups being compared on one continuous measure.
Decision rule. Reject Ho if p is less than 0.05.
If the sub-problem compared three or more strands, the hypothesis wording barely changes but the test becomes a one-way ANOVA, followed by a post-hoc test to identify which pairs differ. Running three separate t-tests instead inflates the chance of a false positive, which is a design error the hypothesis wording will not save you from. Our guide to quantitative research designs maps designs to their matching treatments.
Worked Example 3: Directional vs Non-Directional
Illustrative.
Non-directional (two-tailed), the usual choice: Ha. There is a significant relationship between daily screen time and self-reported sleep quality.
Directional (one-tailed): Ha. Higher daily screen time is significantly associated with lower self-reported sleep quality.
The directional version is a stronger claim and gives a slightly easier path to significance, which is exactly why it needs justification. It is defensible only when prior literature establishes the direction convincingly, and that literature must appear in your review of related literature before you rely on it.
What is never defensible is switching to one-tailed after seeing that the two-tailed result missed significance. Advisers and panels look for this, and unlike most methodological slips it reads as dishonesty rather than inexperience.
Worked Example 4: An Intervention Hypothesis
Illustrative, constructed for this guide.
Illustrative sub-problem. Is there a significant difference between the pre-test and post-test scores of Grade 8 students in rational algebraic expressions after a two-week peer tutoring intervention?
Ho. There is no significant difference between the pre-test and post-test scores of the respondents.
Ha. There is a significant difference between the pre-test and post-test scores of the respondents.
Test. Paired-samples t-test, since the same students are measured twice.
Two details matter here. The word "paired" is not decoration; using an independent-samples t-test on the same people measured twice throws away the pairing and answers a different question. And because intact class sections are rarely randomly assigned, this design is quasi-experimental, not experimental, and the conclusion must be worded accordingly.
Reading the Result Without Overclaiming
When the test comes back, three sentences are all you need, and each has a correct form:
- Decision. "The null hypothesis is rejected." Not "the hypothesis is proven."
- Statistical statement. "There is a significant relationship between X and Y (r = 0.42, p = 0.003)." Report the statistic, the degrees of freedom where applicable, and the exact p-value.
- Interpretation. What that means for your respondents, in your setting, at that time.
Then stop. The American Statistical Association's 2016 statement on p-values sets out six principles, and two of them are worth taping to your monitor. Principle 2: p-values "do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone." Principle 5: a p-value or statistical significance "does not measure the size of an effect or the importance of a result."
In plain terms: a significant result does not mean your finding is large, important, or causal. A correlational design cannot establish cause however small the p-value gets, which is why "screen time causes poor sleep" would be an indefensible conclusion from Worked Example 1 while "screen time is significantly associated with lower reported sleep quality among these respondents" is fine.
Common Mistakes That Cost Marks
- A hypothesis for a descriptive sub-problem. Nothing to test.
- A hypothesis that names variables the questionnaire does not measure. Check it against your independent and dependent variables and your instrument.
- "Accept the null hypothesis" written as though the null were proven. Follow your manual's wording, but understand it means failure to reject.
- Choosing alpha after seeing the p-value. The level of significance is set in Chapter 3, in advance.
- Claiming causation from a correlational test. The most common overclaim in Chapter 5.
- A hypothesis with no matching sub-problem, or a relational sub-problem with no hypothesis. They pair one to one.
- Hypotheses in a qualitative study. Signals the design was borrowed rather than chosen. See qualitative vs quantitative research if you are unsure which you are doing.
A Short Checklist
- List your relational and comparative sub-problems. Each gets exactly one hypothesis.
- Write each in the null form, and add the alternative if your manual requires it.
- Name the statistical test beside each hypothesis, before data collection.
- Fix the level of significance and write the decision rule into Chapter 3.
- Check that every variable named in a hypothesis is actually measured by an item in your instrument.
- Draft the conclusion sentence you would write under each outcome. If either version sounds like an overclaim, fix the hypothesis now.
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Disclaimer: departments differ on where hypotheses are placed, whether both forms must be written, and what wording is required for the decision. Confirm your own research manual before finalising this section.
Sources
- DepEd — K to 12 Senior High School Applied Track, Practical Research 2 Curriculum Guide — competency CS_RS12-If-j-8, "lists research hypotheses (if appropriate)"
- DepEd — K to 12 Senior High School Applied Track, Practical Research 1 Curriculum Guide
- American Statistical Association — Statement on Statistical Significance and P-Values (March 7, 2016) — the six principles quoted above; published as Wasserstein and Lazar (2016), The American Statistician 70(2), 129-133
- SchoolFinderPH — Statement of the Problem Guide
- SchoolFinderPH — Quantitative Research Designs
- SchoolFinderPH — Analysing Data in a Student Research Paper
