From an Observation to a Researchable Question
Out of everything we could ask about Mateo, which questions can science actually answer, and how do we sharpen one of them?
💡 A question is researchable only when real, observable data could answer it, and PICO is how we sharpen it.
What you will learn
Goal: Students will convert an observation about Mateo into a focused, answerable using the PICO framework, and separate researchable from non-researchable questions.
- A question is researchable when it can be answered by collecting and analyzing real, observable (empirical) data; questions of blame, single fate, or value are not.
- PICO sharpens a vague question into Population, Intervention or exposure, Comparison, and Outcome.
- A scientific question must be falsifiable: some possible result could prove it wrong.
- Three of the team's questions map onto real studies: the TOPS surgical-timing trial, a five-country of stress, and a Danish twin heritability study.
Model: Mateo's intake note and a wall of questions
Here is the team's intake summary (composite patient, no real patient data): Mateo, born at term, of the left lip and , finds no other birth defects, parents are unaffected, family history is limited and unclear.
When the team brainstormed, they wrote every question on the whiteboard. A: Why did this happen to Mateo specifically? B: Was it something the parents did wrong? C: Among babies with a , does repairing the palate earlier rather than later lead to fewer speech problems by age 5? D: Is Mateo's future a good one? E: In mothers during pregnancy, is high life stress associated with higher odds of having a baby with an orofacial cleft? F: How heritable is cleft lip and palate, that is, how much of the risk is explained by genes? Three of these (C, E, F) map onto real published studies. C is the TOPS surgical-timing trial [PMID:37646677]. E is a five-country of stress and clefts [PMID:37118740]. F is a Danish twin heritability study [PMID:21423016]. The others (A, B, D) are questions science struggles with as written.
Explore (work the model before reading on)
- List which questions (A through F) name a specific group of people, a specific thing to compare, and a specific outcome to measure.
- Which questions could be answered by collecting and counting real data? Which could not?
- Look at B (something the parents did wrong) and E (is high life stress associated with higher odds of a ). Why is E answerable and B is not? What did E add or remove?
- Questions C, E, and F each became a real study. What do all three share that A, B, and D lack?
- Take question A (Why did this happen to Mateo specifically). Rewrite it so a study could actually answer it. What did you have to change?
- In one sentence, what makes a question researchable rather than just interesting?
Guided notes
Researchable versus interesting
- A question is researchable when it can be answered by collecting and analyzing real, observable data; we call data-based answers ____ (empirical) answers.
- Questions of blame, single fate, and value matter to people, but a study cannot settle them by measurement.
Sharpening with PICO
- P = ____ (Population): who or what we study. I = the Intervention or exposure we are interested in.
- C = ____ (Comparison): what we compare against. O = ____ (Outcome): the specific thing we measure.
The falsifiability rule
- A good scientific question must be ____ (proven wrong); if no possible data could ever change the answer, it is not scientific.
- TOPS answered a tightly PICO-shaped question and reported a clear measured result, fewer speech problems with earlier repair (8.9% versus 15.0%).
Reading the Research
- Skim the title and abstract first to get the gist.
- Circle the one sentence that states the main claim.
- Box the evidence the authors give for that claim.
- Mark one sentence that confuses you, and move on.
Vocabulary (the same words your classes use)
Vetted readings for this lesson
- Gamble C, et al. 2023. Timing of Primary Surgery for Cleft Palate (TOPS trial). N Engl J Med. [PMID:37646677]
- Sabbagh HJ, et al. 2023. COVID-19 risk factors and orofacial clefts, five Arab countries: case-control study. BMC Oral Health. [PMID:37118740]
- Grosen D, et al. 2011. Risk of Oral Clefts in Twins. Epidemiology. [PMID:21423016]
Track your progress today
Check these off as you work through the lesson, then submit. This tells Mr. Mendoza how you're doing so he can help the class. It does not replace turning in your producible.
Use the code Mr. Mendoza gave you, not your name. Saved on this device.
- Read the Model and answered the Explore questions.
- Filled in the guided notes in my own words.
- Defined the new vocabulary with an example.
- Built the producible: Pick one open question about Mateo and write it as a clean PICO question with all four parts (P, I, C, O) labeled, then write one sentence stating what kind of data you would collect to answer it and whether the outcome is something you could actually measure.
- Wrote my Claim, Evidence, and Reasoning exit ticket.
Exit ticket (Claim, Evidence, Reasoning)
- Claim: One question on the team's whiteboard is researchable and one is not.
- Evidence: The researchable one is ____, with P = ____, I = ____, C = ____, O = ____; the non-researchable one is ____.
- Reasoning: The second question cannot be answered by collecting data because it asks about ____ (blame, single fate, or value) rather than a measurable outcome.
| Criterion | Proficient | Developing | Beginning |
|---|---|---|---|
| Complete | Every required part of the artifact is present and filled in. | Most parts are present, but one is missing or left blank. | Several parts are missing. |
| Accurate | The science and data are correct and match the evidence. | Mostly correct, with a small factual slip. | Key science or data is wrong. |
| Scientific reasoning (CER) | States a claim, backs it with specific evidence, and explains the reasoning. | Has a claim and evidence, but the reasoning is thin or missing. | Gives an answer with no evidence or reasoning. |
| Professional communication | Clear, organized, and labeled the way a clinician or scientist would write it. | Readable but disorganized or missing labels. | Hard to follow. |
| Submitted | Turned in the right way (Schoology for routine work) and confirmed. | Turned in, but in the wrong place or unconfirmed. | Not turned in. |
- CompleteProficient: Nothing is left blank: the model fills every part of "Pick one open question about Mateo and write it as a clean PICO question with all four parts (P, I, C, O) labeled, then write one sentence stating what kind of data you would collect to answer it and whether the outcome is something you could actually measure.".
- AccurateProficient: Every number and claim matches the case evidence.
- Scientific reasoning (CER)Proficient: It names a claim, cites the specific evidence, and explains the reasoning, not just the answer.
- Professional communicationProficient: It is organized and labeled like a real chart note.
- SubmittedProficient: It would be turned in on Schoology and confirmed.
Where this leads: careers
What's next: We turned an observation into a sharp PICO question. But a question is still not a prediction. How do we turn a PICO question into a hypothesis we can actually prove wrong, with variables we control and a clear way to fail?