28 From Results to Claims
This chapter is part of a book in active development and has not yet been through the author’s review. Content may change as the review advances.
The research decision. Your analysis printed a number. Now you decide the sentence that number lets you assert: the headline you will show in public and defend out loud, worded so its verb stays inside what your design established and its uncertainty rides in the same eye-span. You choose the words. You lock them. You never let a tool talk your claim past its boundary.
28.1 Why this decision matters
The decision on the table: the verb in your headline, and how far your evidence lets it reach.
“In the poster hall I point at one number and ask where it came from and what it lets you say. If the headline reads ‘lowers blood pressure’ but the study only watched who chose to walk, I stop reading. A claim that outruns its evidence tells me to distrust the whole board.” — a research-integrity judge working a poster session
A result is not yet a claim. Your analysis prints a number; your headline asserts a sentence. The gap between them is where most projects break, because the sentence almost always wants to say more than the number earned. The judge above is not testing your statistics. She is testing whether your words match the evidence underneath them. This chapter is the habit that closes that gap before a stranger finds it open.
28.2 The concept
Start with the two words this chapter lives between. A result is a number your analysis produced, such as a difference of 6 points between two group averages. A claim is a sentence you assert and will defend, such as “the walking group had lower blood pressure.” Turning the first into the second is a decision, not a formatting step, because you choose how much the sentence is allowed to say.
Four ideas govern that choice, and keeping the first three apart is what stops overclaiming before it starts.
Your question is what you set out to learn, and its kind comes from its own words. Example: “does walking lower blood pressure?” asks what an intervention would change, so it is causal, and it stays causal no matter which data you managed to collect.
Your identification status is whether this design can actually deliver that answer (Blair et al. 2023). Example: if you watched who already chose to walk, nothing separates the walking from the kind of person who walks, so your status is not identified by this design under the assumptions I can defend. Notice what that status does not say. It never repeats the word “causal”, because the kind belongs to the question and the status belongs to the design; writing “causal and unidentified” as one status quietly fuses the two fields you just separated.
Your result is the quantity this evidence did produce. Here that is a descriptive association between walking and blood pressure, which is real and worth reporting, and which sits beside the causal question rather than replacing it.
Written out, the four fields make a card you can reuse on any project:
Question kind: causal. Identification status: not identified by this design under stated assumptions. Result: observed association. Claim boundary: association only.
Your claim boundary is what those three together license you to write, and it lives in the headline verb. Example: with no identification argument, you write “was associated with lower,” never “lowered.” Read the condition carefully: what earns a causal verb is a defended identification argument (Hernán and Robins 2020). Randomizing is the cleanest way to get one, and the observational-causal pathway earlier in the book shows designs that carry causal answers when their assumptions are argued and defended. What you may never do is use the verb without the argument.
One more idea makes the claim’s honesty visible. Uncertainty in the same eye-span means the interval or caveat sits right beside the claim, not in a footnote (Wasserstein and Lazar 2016). Example: “about 6 points lower (95% interval: 1 to 11)” printed under the headline, so a reader meets the doubt at the same moment as the number. The numbers in this chapter’s examples are constructed to make the point; the uncertainty lesson you already worked through builds intervals properly. A bound a reader has to hunt for is a bound doing hidden work.
One more habit backs all of this. Traceability means every number you show walks back to the exact notebook cell that produced it. Example: the 6-point gap on your board points to the one line of code that computed it, and you can show that line when a judge asks. A number you cannot trace cannot go up.
28.3 A worked example
A biology student runs a small human-physiology pilot. Forty volunteers wear a step tracker for a month. Twenty walk at least 8,000 steps a day; twenty do not. Their average resting systolic blood pressure comes out at 128 in the active group and 134 in the less-active group, a 6-point gap.
The result is that 6-point gap. The claim is the decision. The draft headline reads “Walking LOWERS blood pressure,” drawn as two bars on an axis that starts at 125, which makes the small gap look like a cliff, with the two groups told apart by red and green alone.
Walk the decision. The question behind this study is causal, and it stays causal: you want to know whether walking lowers blood pressure. What the design cannot do is answer it. Nobody was assigned to walk, so the active group may differ in age, diet, or baseline health, and the comparison leaves that untouched. Fill the card: question kind causal, identification status not identified by this design, result an observed association. Those three together fix what the claim may say. The verb “lowers” claims a crossing the design never paid for, and the honest verb is “was associated with lower.” The truncated axis is the next problem: bars encode magnitude, so starting them at 125 makes a 6-point gap look like a cliff. Start the bars at a meaningful zero, or better, plot the two group averages as points with their intervals on a blood-pressure scale a clinician would recognize. The interval reported here runs roughly 1 to 11 points (constructed for the example), wide enough that the honest headline must carry it in the same eye-span. And each bar gets a direct value label, so a color-blind or screen-reader reader receives the number, not just the hue.
The locked claim: “In this sample, more-active volunteers averaged about 6 points lower resting systolic blood pressure (95% interval: 1 to 11); this is an observed association, and this design does not identify whether walking caused the drop.” Same result, a claim that survives the judge.
What licenses a causal verb is the identification argument, not the size of the gap you observed (Hernán and Robins 2020).
The block below builds the forty volunteers and prints the gap along with one thing the poster never showed: how the two groups differ before you compare them.
import numpy as np, pandas as pd
SEED = 464
rng = np.random.default_rng(SEED)
# Forty volunteers who CHOSE how much they walk. Age drives both.
n = 40
age = np.r_[rng.normal(31, 7, 20), rng.normal(43, 7, 20)] # active are younger
active = np.r_[np.ones(20, bool), np.zeros(20, bool)]
systolic = np.round(112 + 0.5 * age + rng.normal(0, 6, n))
gap = systolic[~active].mean() - systolic[active].mean()
print(f"active group mean systolic : {systolic[active].mean():.0f}")
print(f"less-active group mean systolic : {systolic[~active].mean():.0f}")
print(f"observed gap : {gap:.0f} points")
print(f"mean age, active vs less-active : {age[active].mean():.0f} vs "
f"{age[~active].mean():.0f}")
print("\nnobody was assigned to walk. the age line is one difference you can")
print("see; the design leaves every unseen one untouched, so the verb stays")
print("'is associated with'")28.4 An AI failure case
You paste your honest headline into a tool and ask it to make it “punchier.” It returns, with total confidence, “Walking Reduces Blood Pressure by 6 Points.” It reads beautifully and it is wrong. The tool upgraded your verb from an association to a cause and dropped your interval, a silent scope change dressed up as a copy edit.
You catch it by comparing the new verb to what your design earned. Your question was causal and stays causal, but nobody was assigned to walk, so nothing identifies that answer and “reduces” claims a warrant you never held. You also notice the “6 points” now stands alone with no interval beside it. You revert to “was associated with about 6 points lower (95% interval: 1 to 11).” The fluent rewrite reviewed your style; only you can review your claim.
28.5 It is your turn
You are working inside Studio 9: Write, bound, and disclose. Keep what you write here; the studio’s milestone chapter is where it joins the other lessons’ pieces into one artifact you can defend.
After the last chapter you know where your confidence actually stops; this step turns each of your results into a sentence you would print and defend.
The hands-on half of this section lives in the chapter’s companion notebook: open it in Colab with the badge at the top, and work the steps there.
Commit your own answer first, then delegate. Each prompt is a checkable job, never a request for a verdict.
Expect to run each one more than once. You read what comes back, name the part that is wrong, and ask again with that correction in the prompt. That back-and-forth is the method, not a sign you prompted badly. Some tools now run several rounds on their own before showing you anything, which raises the stakes on your last step rather than lowering them, because more of the reasoning happened where you could not watch it.
Three calls stay yours. You choose the verb in your headline, the one word that fixes your claim boundary. You make the defend-or-concede call when a question pushes on that boundary out loud. And you make the lock decision: the judgment that every number is traced, every citation resolves, and the claim is ready to be final. A tool can draft questions and hunt sources, but it cannot decide what your evidence licenses or when your claim is earned.
List every result you might put in front of a reader, then write the claim sentence each one licenses.
Check every verb against what your design established, and downgrade the ones that reach too far. “Caused” becomes “was associated with”; “predicts” becomes “flagged.”
Red-team the verb.
Here is my written headline: "[paste it]." Act as a hostile reviewer and find every place the verb claims more than an observational design licenses. Do not rewrite it for me; list the exact words so I can fix them myself.After running, verify: read each flagged word against your compass position and keep only the objections your design actually forces. Counters silent scope change (a claim quietly upgraded from association to cause).
Write a boundary line beside each claim naming what it does not establish, and put the uncertainty in the same eye-span as the number.
Turn your result into a checklist of hard questions.
Here is my headline claim: "[paste it]". My question is causal, my design is observational and does not identify it, and what I actually estimated is a descriptive association. Generate the eight hardest questions a skeptic would ask, and mark which single audit each one belongs to: claim boundary, figure honesty, uncertainty, or accessibility.After running, verify: map every question to an audit and check it against your printed numbers; a missing audit is a gap the tidy list hid. Counters illusion of completeness (a long, organized list that skips the one flaw that matters most).
Trace every number back to the cell that produced it and cut the ones you cannot walk back. Then pick your lead claim, the sentence you would say first if you had ten seconds.
Log it in your AI Research Ledger, and verify at least one claim with a named method from the Verification Guide. An AI reviewer may run the check with you; the decision to accept or reject stays yours.
Locate the real source behind a physiology claim.
Act as a literature assistant in human physiology. My headline claims regular walking is linked to lower blood pressure. Name up to three peer-reviewed sources or clinical guidelines that support this, with title, authors, year, and venue. Only list work you are confident exists; mark anything uncertain.After running, verify: open each source in a library database and confirm it exists and says what you claim. Counters confident fabrication (an invented citation arrives as confidently as a real one).