Specificity vs sensitivity
Open your materials, follow the steps, then turn in your work.
Distinguish specificity from sensitivity and use your ELISA results to discuss how good the test is.
1. Open your materials
Use the materials named in the first step below. Open lesson resources.
2. Start the work
Write plain definitions: sensitivity catches true positives, specificity avoids false positives.
Show all 6 required steps
- Write plain definitions: sensitivity catches true positives, specificity avoids false positives.
- Sort your ELISA results into true positives, true negatives, and any apparent errors.
- Decide whether any odd result looks like a sensitivity or a specificity problem.
- Explain why a screening test often favors sensitivity over specificity.
- Connect your controls to how you would trust the test's specificity.
- Write one sentence judging how trustworthy your ELISA run was and why.
Lost your place? Lost track? You should have plain definitions of sensitivity and specificity written down and your ELISA wells sorted into true positives, true negatives, and apparent errors. If not, do those first, then decide whether each odd result is a sensitivity or specificity issue and write your one-sentence trust judgment.
Check your work before submitting
- You will be able to define sensitivity and specificity.
- You will be able to classify results as true or false positives and negatives.
- You will be able to judge a test's reliability from its results.
Before lab work: read the safety rules
- Wear the required PPE, keep the bench clear, handle equipment only as directed, and know where the eyewash, sink, and spill kit are before you start.
- Human samples and data stay private: label with a code, never a name, and dispose of materials in the correct waste container, then wash your hands.
3. Turn in your work
DueCheck Schoology- Hand in
- 2x2 classification table sorting ELISA results into true/false positives and negatives, plus a one-sentence reliability judgment citing controls.
How to submit and name your file
Bring to Friday's report writing session; include in the lab report.
In Schoology, open your course and the assignment for this lesson. Attach your file, select Submit, and check that it appears in the submission.
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How this lesson connects
Keep using what you learned last class: A defensible biomedical decision separates scientific evidence from value judgments, identifies who may benefit or be burdened, and states the uncertainty and tradeoffs that remain. Today: Sensitivity and specificity trade off against each other, so the right balance is set by whether missing a sick person or alarming a healthy one causes more harm for that disease.
Optional: listen or watch a unit review▸
Need help? Warm-up, timing, and directions▸
💡 Big idea: Sensitivity and specificity trade off against each other, so the right balance is set by whether missing a sick person or alarming a healthy one causes more harm for that disease.
- 0-10 minWrite plain definitions of sensitivity and specificity; draw the 2x2 table (true positive, true negative, , )
- 10-25 minSort Wednesday's results into the four categories using your plate photo and
- 25-40 minClassify any anomalous results: sensitivity problem (missed a true positive) or specificity problem (flagged a true negative)?
- 40-55 minExplain in writing why a screening test favors sensitivity; connect to Monday's false-results bioethics discussion
- 55-68 minConnect your controls to the test's specificity: did the stay negative? Explain what it proves
- 68-80 minWrite one trustworthiness judgment sentence; share with a partner and defend your reasoning
- • A test that is 99% accurate sounds great; but if the disease is rare, most positive results could still be false positives.
- • Sensitivity and specificity are the two numbers that determine whether a test is useful for a given situation.
- • Today you apply both concepts directly to your own data from Wednesday.
- • Exit goal: a completed 2x2 classification table and a trustworthiness judgment sentence.
- • Sensitivity measures how well a test catches true positives (sick people who test positive); a sensitive test misses few sick people.
- • Specificity measures how well a test avoids false positives (healthy people who test positive); a specific test rarely flags healthy people as sick.
- • Screening tests (population-level) favor high sensitivity; confirmatory tests favor high specificity.
PLTW connection and today's work
Open Activity 1.1.6 Final Diagnosis in myPLTW and use your ELISA well data to plot the standard curve and read your unknown.
Today's stopping point: Well-color data should be recorded (Wednesday); standard curve plotted and concentration estimated today.
PLTW activity titles identify the course connection. If your account will not open, use the posted materials for today and tell Mr. Mendoza. Do not mark an online activity complete unless you completed it.
Use the turn-in directions at the top of this page. Do not create a second submission unless your teacher asks for one.
Show another explanation or a smaller first step
Need help? Choose a starting point
Finish the assigned lab safely before starting extra practice.
Lesson resources: reading, slides, and vocabulary▸
The deck carries the prior idea forward, lets you inspect an analogy, maps the rule to biology, and ends with the same evidence decision and exit ticket used on this page.
Generated from this lesson's canonical data with a red-team citation check.
A defensible biomedical decision separates scientific evidence from value judgments, identifies who may benefit or be burdened, and states the uncertainty and tradeoffs that remain.
Sensitivity and specificity trade off against each other, so the right balance is set by whether missing a sick person or alarming a healthy one causes more harm for that disease.
A smoke alarm detects signs of fire but can also react to burnt toast.
- What does the alarm detect?
- What creates a false alarm?
- What evidence is needed before declaring a fire?
A screening signal changes what to investigate next; it does not automatically prove the cause.
Biomedical tests have measured performance and biological sampling limits that a household alarm does not capture.
- • Alarm signal maps to a test result.
- • Burnt toast maps to a .
- • Inspection maps to confirmation or the next test.
Driving question: Using your own results, which of your wells are true positives, true negatives, or errors, and does any odd result point to a sensitivity problem or a specificity problem?
What you already know: A defensible biomedical decision separates scientific evidence from value judgments, identifies who may benefit or be burdened, and states the uncertainty and tradeoffs that remain.
New idea: Sensitivity and specificity trade off against each other, so the right balance is set by whether missing a sick person or alarming a healthy one causes more harm for that disease.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Specificity vs sensitivity. Use it with E1-E3; it is a model or context image, not experimental or patient data. What to notice: Trace the labeled testing, treatment, or biological process and identify where evidence limits the decision.
- Observe or measure the relevant feature in specificity vs sensitivity.
- Organize the observation with a stable evidence ID.
- Apply this rule: A screening signal changes what to investigate next; it does not automatically prove the cause.
- Choose the option the evidence supports and state the limit of the conclusion.
Real biomedical example: Using your own results, which of your wells are true positives, true negatives, or errors, and does any odd result point to a sensitivity problem or a specificity problem?
What the evidence supports: E1-E3 and F1 support the daily take-home when the response meets the stated success criteria.
What it cannot prove: The package does not support claims beyond this lesson's or any real patient diagnosis.
- • : A sample known to produce a result, included in an experiment to confirm that the test is working correctly.
- • : A sample in an experiment expected to show no effect, used as a baseline to confirm that any result in the test sample is real.
- • specificity: A test's ability to correctly identify people who do not have a condition, giving few false positives.
- • sensitivity: A test's ability to correctly identify people who truly have a disease, measured as the share of real cases that the test flags as positive.
- • : The first added in a test that binds directly to the target molecule, marking it so it can be detected later.
- • : An that binds to a and carries a tag, such as a dye or , to make the target visible in a test.
Use it now: Choose one decision option. Cite E1 and E3, then explain how the rule connects the evidence to your choice.
Go further, optional: The source links below are optional enrichment. Every fact required for today's local evidence decision appears in this lesson package.
An uses selective binding and controls to produce a measurable signal, but performance, cross-reactivity, sampling, and the decision threshold limit what a result can establish.
Limit: A classroom result does not establish a clinical diagnosis or an exact target concentration unless the validated and support that use.
A screening signal changes what to investigate next; it does not automatically prove the cause.
Limit: Biomedical tests have measured performance and biological sampling limits that a household alarm does not capture.
You can define sensitivity and specificity.
Limit: E3 defines the classroom product or success criterion. It is not independent scientific evidence and cannot justify a clinical or causal claim.
PLTW-GEND-2026-09-25 · Simulated classroom evidence scenario
Your role: medical interventions team member
Decision: Your team must decide what the evidence from specificity vs sensitivity supports before submitting the labeled and result claim named on today's page.
- • Write the report so the controls and the - decision threshold set the limits of what your result can claim.
- • Report the concentration from the most colorful well, because standard curves work best when the signal is strongest.
- • Omit the controls from the final report and state only whether the patient had the disease or not.
Response: State one choice, cite at least two evidence IDs, explain the rule that connects them, and add one limitation. Submit it as the labeled and result claim.
Claim ceiling: Today's evidence supports a classroom claim about specificity vs sensitivity. It cannot prove causation, diagnose a real patient, or justify action outside this room.
Reason for review: Your team must decide what the evidence from specificity vs sensitivity supports before submitting the labeled and result claim named on today's page.
Context: Sensitivity and specificity are two separate measures of a test's reliability, and the balance you want between them is set by the cost of each error for that particular disease.
- • T1: Write plain definitions: sensitivity catches true positives, specificity avoids false positives.
- • T2: Sort your results into true positives, true negatives, and any apparent errors.
- • T3: Decide whether any odd result looks like a sensitivity or a specificity problem.
- • T4: Explain why a screening test often favors sensitivity over specificity.
- • T5: Connect your controls to how you would trust the test's specificity.
- • T6: Write one sentence judging how trustworthy your run was and why.
- • E1: An uses selective binding and controls to produce a measurable signal, but performance, cross-reactivity, sampling, and the decision threshold limit what a result can establish.
- • E2: A screening signal changes what to investigate next; it does not automatically prove the cause.
- • E3: You can define sensitivity and specificity.
Measurements: Use only the measurements, units, graph, or counts supplied in today's task. No additional patient measurement is implied.
Figure finding: Teaching diagram for Specificity vs sensitivity. Trace the labeled testing, treatment, or biological process and identify where evidence limits the decision. This is a teaching model, not patient or experimental data.
Uncertainty: This is a composite classroom scenario. Missing history, measurements, or confirmation tests remain unknown and limit the conclusion.
For a linear , y = mx + b. To estimate an unknown concentration, use x = (y - b) / m.
A is y = 0.40x + 0.10. An unknown signal is 0.90. x = (0.90 - 0.10) / 0.40 = 2.0 concentration units.
Signal units belong on y. Concentration units belong on x. Confirm the unknown falls inside the standards before interpreting it.
Use the equation or graph supplied today to estimate one unknown. Show the substitution, concentration unit, and range check.
- • The solution must address the stated need in specificity vs sensitivity.
- • The decision must be supported by E1-E3.
- • The final product must make the success criteria visible.
- • Complete the work inside the 80-minute block.
- • Use only supplied or teacher-approved materials and evidence.
- • Do not trade , accessibility, or privacy for speed.
- • and evidence quality: must pass before scoring other criteria.
- • User need and effectiveness: highest scored criterion.
- • Time, cost, and ease of use: compare only after and effectiveness pass.
Test evidence: For each option, record the E1-E3 result that supports or fails each criterion. Do not assign a score without a named observation.
- Version or option tested
- Criterion met or missed
- Evidence ID and result
- Revision made
- Reason for the revision
- Need and user
- Criteria and constraints
- Chosen option and evidence
- Test result
- Revision and reason
Students often think Students treat sensitivity and specificity as the same thing (just 'accuracy') and assume a good test maximizes both at once.. The trap: Sensitivity and specificity are different and they trade off, because moving the threshold to catch more sick people (higher sensitivity) also flags more healthy people (lower specificity); no single dial makes both perfect.
Definitions: sensitivity catches true positives (sick people who test positive); specificity avoids false positives (healthy people who test positive).
Reliability judgment: because my positive control turned color and my negative control stayed clear, I trust this run's specificity, and the one odd result looks like a sensitivity issue (a known-positive sample that read weak), not contamination.
| Actually sick | Actually healthy | |
|---|---|---|
| Test positive | True positive | False positive |
| Test negative | False negative | True negative |
This model shows the level of evidence and organization needed to complete: A two-by-two table sorting ELISA results into true and false positives and negatives, with a one-sentence judgment of the run's reliability that cites the controls.
- Name the variables and include units.
- Enter observations without changing the raw values.
- Check labels, calculations, and patterns before interpreting the data.
Keep the structure. Replace the question, facts, measurements, and evidence. Then recheck units, vocabulary, and whether the conclusion goes beyond the evidence.
Also due today: Bring to Friday's report writing session; include in the lab report.
- CER:
- Claim, Evidence, Reasoning: make a claim, back it with evidence, explain your reasoning.
- SOP:
- Standard Operating Procedure, the exact steps to follow (especially in a lab).
- Tracker:
- Your PLTW progress log where you record completed evidence.
- myPLTW:
- The PLTW course site where you do the online activities. Find it in Clever with your Microsoft sign-in, right next to Schoology.
Tap the speaker to hear a term. Add two of these to your notebook glossary with a definition and an example in your own words.
Pick just 2 or 3 words from today and make them yours: write what each one means in your own words, name the context clue or evidence that helped, then give one example from what you actually did in Specificity vs sensitivity. Try your own words first; the glossary is there if you get stuck. This is voluntary and counts as extra credit, so keep it short.
Saved on this device. Show Mr. Mendoza or add these to your notebook glossary to claim the extra credit.
Classroom documents for this lesson are posted in Schoology. Open Clever, then Schoology, and find each one by the name shown on its card.
Open this when the class reaches this activity and use it to complete the required lesson artifact.
Placement rationale
Matched lab, controls, diagnosis limits by path:Medical-Interventions/Unit-1_How-to-Fight-Infection/1.1_The-Mystery-Infection; keywords:elisa, lab. Score 138. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Placement rationale
Matched lab, controls, diagnosis limits by path:Medical-Interventions/Unit-1_How-to-Fight-Infection/1.1_The-Mystery-Infection; keywords:elisa, lab. Score 138. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Open this when the class reaches this activity and use it to complete the required lesson artifact.
Placement rationale
Matched lab, controls, diagnosis limits by path:Medical-Interventions/Unit-1_How-to-Fight-Infection/1.1_The-Mystery-Infection; keywords:elisa, lab. Score 138. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Sign in to Clever with your district Microsoft account to open Schoology or myPLTW. Follow today's posted steps. If myPLTW will not open, use the posted alternative and tell Mr. Mendoza. Turn in your completed work through the Schoology assignment.
Practice: try a question, then check your answer▸
Claim ceiling for this check: Today's evidence supports a classroom claim about specificity vs sensitivity. It cannot prove causation, diagnose a real patient, or justify action outside this room.
A screening ELISA for an outbreak lets a truly infected person's well stay clear (a negative result). Is this a sensitivity failure or a specificity failure, and why is it especially dangerous for a screening test?
Write an answer and pick a confidence to unlock the key.
Fast retrieval with instant answers, not the commit-then-reveal check above. Try each from memory first: write what you remember about the earlier units, then check yourself here.
Missed class or ready for more?▸
Run this before you touch the bench. It is built from the real lab procedure, so the decisions you make here are the ones you will make with the equipment in your hands.
I can name the procedure's purpose and the evidence I will record. I can state today's specific hazards and the control for each. If this deck does not name them, I ask Mr. Mendoza before I touch anything. My data table is ready before materials are handled.
Finish the checklist before you handle any material.
- • Wear the required PPE, keep the bench clear, handle equipment only as directed, and know where the eyewash, sink, and spill kit are before you start.
- • Human samples and data stay private: label with a code, never a name, and dispose of materials in the correct waste container, then wash your hands.
- 1Before materials are handled, identify the purpose, variables or comparison, controls, measurement units, and stop-work condition.
- 2Write plain definitions: sensitivity catches true positives, specificity avoids false positives.
- 3Sort your ELISA results into true positives, true negatives, and any apparent errors.
- 4Decide whether any odd result looks like a sensitivity or a specificity problem.
- 5Explain why a screening test often favors sensitivity over specificity.
- 6Connect your controls to how you would trust the test's specificity.
- 7Write one sentence judging how trustworthy your ELISA run was and why.
- 8Record each result in the prepared table before interpreting it. Mark missing, repeated, or invalid results truthfully.
- 9Complete the named cleanup and waste route, remove PPE safely, wash hands when required, and confirm the station is ready for the next group.
| Trial or sample ID | Independent condition | Measured result with units | Observation before interpretation | Quality-control note |
|---|---|---|---|---|
Before the procedure, predict the result and cite the rule behind the prediction.
After the procedure, compare the result with the prediction and name one limitation or source of uncertainty.
What today's skills lead to. These are real health-science careers this course builds toward. Tap one to see, on the US Department of Labor's O*NET site, what the job actually involves, what it pays, and how fast it is growing.
The bench work needs equipment you do not have at home. Do the thinking half now: read the procedure, write your prediction, and set up your data table so it is ready.
Back in class. Ask Mr. Mendoza for the class data set, or for a bench slot to run it yourself. Do not submit a Data table with invented numbers.
Class still runs. Complete the online activity above (it's self-guided). Need the concept taught without a teacher? Use this authoritative explainer:
HHMI BioInteractive (preview; use fallback if blocked)- CompleteEvery required part of the artifact is present, nothing left blank.
- AccurateThe science and the data are correct and match the evidence.
- Scientific reasoningYou explain your claim with evidence and reasoning (CER), not just an answer.
- Professional communicationClear, organized, labeled, and written the way a clinician or scientist would.
- SubmittedGo to Schoology to turn this in. Submit one PDF. Put your first and last name in the document header. Name the file: FirstName LastName - Assignment Title - YYYY-MM-DD.pdf. If you cannot get in, see Mr. Mendoza. Do not skip the work.
- Error analysis and method · counts doubleName a specific limit of the method and how it moved your result, and compare what you predicted to what happened. "Human error" does not count; say what about the procedure or instrument caused it.
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