Week 8
“A man who uses force is afraid of reasoning.”
Recap…
Last week, we untangled the art of setting clear objectives. We agreed that objectives are like the Google Maps directions of your thesis journey: precise, purposeful, and pointing you toward your destination. Now, we take the next logical step, stating your hypothesis.
Introduction
Every great research study is driven by curiosity, but curiosity without a hypothesis is like a car without a steering wheel. You may move, but in circles, or worse, into a ditch. A hypothesis provides focus, gives your study predictive power, and frames how your data will eventually be interpreted.
Yet, for many fellowship residents, the word “hypothesis” triggers mild anxiety. Some confuse it with the research question, others overcomplicate it with jargon, and a few write hypotheses that sound more like philosophical musings than testable predictions.
Today, let’s break down the anatomy of a good hypothesis: what it is, what it isn’t, and how to craft one that can stand before both the WACP and GCPS assessors without being politely returned with red ink.
What Exactly is a Hypothesis?
A hypothesis is a testable statement that predicts the relationship between two or more variables. It is not just an educated guess; it is a scientifically grounded expectation based on existing evidence.
For instance:
- Research question: What factors predict mortality in severe malaria among children under five?H
- Hypothesis: Children under five with severe malaria who present with hypoglycemia have a higher risk of mortality compared to those without hypoglycemia.
The former is curious. The latter is precise and testable.
Null vs. Alternative Hypotheses
Every good hypothesis comes in pairs, like gari and beans.
Null hypothesis (H₀): There is no association, effect, or difference.
Example: Hypoglycemia does not increase mortality risk in children with severe malaria.Alternative hypothesis (H₁): There is an association, effect, or difference.
Example: Hypoglycemia increases mortality risk in children with severe malaria.
Why both? Because statistics is a skeptical friend. It only allows us to “reject” or “fail to reject” the null, never to “prove” the alternative.
Characteristics of a Good Hypothesis
A useful checklist (adapted from Hulley et al., 2013):
- Testable: You should be able to design a study that collects data to support or refute it.
- Clear and specific: Avoid vague wording like “improves,” “affects,” or “influences” without direction.
- Grounded in literature: No free-floating speculations. Use prior studies to justify.
- Relevant: The outcome should matter clinically or scientifically.
Common Pitfalls Residents Make
- Confusing objectives and hypotheses: Objectives describe what you want to achieve; hypotheses predict what you expect to find.
- Being too broad: “Socioeconomic status predicts malaria mortality”, too vague. Which aspect? Income? Maternal education? Housing?
- Writing untestable statements: “Children with severe malaria suffer because of poverty.” True, perhaps, but not statistically testable in a single fellowship project.
- Forgetting the null: Some candidates only state the alternative. Remember, assessors love to see both sides.
When a Hypothesis May Not Be Needed
Not every proposal requires a hypothesis. For instance, exploratory or descriptive studies (such as chart reviews to document clinical patterns, or surveys assessing the prevalence of a condition) are better framed with research objectives rather than hypotheses. Similarly, qualitative studies often aim to generate insights rather than test predictions, so a clear set of objectives is sufficient.
Example Hypothesis Section
Study topic: Predictors of mortality in severe malaria among children under five years in Kumasi, Ghana.
Null hypothesis (H₀): Clinical and laboratory features such as hypoglycemia, severe anemia, or cerebral malaria are not associated with increased mortality risk among children under five years with severe malaria.
Alternative hypothesis (H₁): Clinical and laboratory features such as hypoglycemia, severe anemia, or cerebral malaria are associated with increased mortality risk among children under five years with severe malaria.
Notice the clarity, the linkage to measurable variables, and the fact that these can be directly tested with data.
In Summary…
A hypothesis is your study’s backbone. It transforms curiosity into a testable, measurable statement. Good hypotheses are clear, specific, and rooted in literature. They come in pairs (null and alternative) and directly map onto your objectives.
Without one, your study risks becoming a fishing expedition in the data ocean, lots of activity, very little catch. With a solid hypothesis, you set yourself up for meaningful findings, whether your results surprise you or confirm your expectations.
Next Week…
Next week, we dive into “Taming the Literature Review” — the part where your laptop has 47 open tabs, your EndNote crashes, and your friends wonder if you’ve disappeared into a PDF jungle.
Until then, remember: your hypothesis is not your destiny, it’s just your best bet. So, write it boldly, test it carefully, and let the data speak.
See you next week, and may your null hypothesis be rejected only when it truly deserves it!
References
- Hulley SB, Cummings SR, Browner WS, Grady DG, Newman TB. Designing Clinical Research. 4th ed. Philadelphia: Lippincott Williams & Wilkins; 2013.
- Ranganathan P, Aggarwal R. Hypothesis testing: An overview. Indian J Crit Care Med. 2018;22(10):757–762. doi:10.4103/ijccm.IJCCM_369_18.
- Thiese MS. Observational and interventional study design types; an overview. Biochem Med (Zagreb). 2014;24(2):199–210. doi:10.11613/BM.2014.022.