Specificity vs sensitivity
Safety gate · before any work
- 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.
Do now
Distinguish specificity from sensitivity and use your ELISA results to discuss how good the test is.
- Hand in
- 2x2 classification table sorting ELISA results into true/false positives and negatives, plus a one-sentence reliability judgment citing controls.
- Where
- Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
You get two school days for every day you were absent, so this deadline moves with you.
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?
Distinguish specificity from sensitivity and use your results to discuss how good the test is.
- • 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.
- In one sentence each, define sensitivity and specificity using the words 'true positive' and '.'
- For an outbreak screen where you want to catch everyone exposed, would you rather have high sensitivity or high specificity? Why?
- 1Write plain definitions: sensitivity catches true positives, specificity avoids false positives.
- 2Sort your results into true positives, true negatives, and any apparent errors.
- 3Decide whether any odd result looks like a sensitivity or a specificity problem.
- 4Explain why a screening test often favors sensitivity over specificity.
- 5Connect your controls to how you would trust the test's specificity.
- 6Write one sentence judging how trustworthy your run was and why.
What did this day actually feel like?
Specificity vs sensitivity
Sensitivity is the share of people who have it that the test catches. Specificity is the share of people who do not have it that the test correctly clears. For one given test, moving the cutoff trades them against each other: loosen it and you catch more real cases and also flag more healthy people. What breaks that trade is not a different cutoff, it is a better test.
This is Monday's ethics question with numbers attached. That ordering is not an accident.
Turned in: data table → Data Tables folder
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
The same day, drawn.
Sensitivity is the share of people who have it that the test catches. Specificity is the share of people who do not have it that the test correctly clears.
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
🛠 Get unstuck · pick your level
Lab day: Tier 1 is the whole class at the bench. No extension today.
🔑 Today's words · 5
Tap a word in the lesson for a plain meaning and one example. Recycled into next week's Do-Now.
Do the work · 80-minute blockfirst 5 min = hook▸
💡 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.
Reading qualitative vs. quantitative color results; false positive/negative risk; control logic. · Specificity vs sensitivity
Day 4 of this lesson. Open this exact section in myPLTW (find it in Clever, Microsoft sign-in), then do the work below.
Do this: Open the standard-curve analysis section of Activity 1.1.5 in myPLTW and plot your results.
Complete the , mark your unknown sample on it, and calculate its estimated concentration.
Well-color data should be recorded (Wednesday); plotted and concentration estimated today.
Standard-curve graph with unknown marked and concentration calculated in notebook.
The official PLTW activity stays inside myPLTW. If myPLTW will not open, use F1 and E1-E3 on this page to complete today's local evidence decision, then make up the official activity when access returns. Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
Check things off as you work, then submit. This tells Mr. Mendoza how you're doing so he can help the class. It does not replace turning in your producible through the submission route shown below.
Use the code Mr. Mendoza gave you, not your name. Saved on this device.
Reading qualitative vs. quantitative color results; false positive/negative risk; control logic. · Specificity vs sensitivity
Open the standard-curve analysis section of Activity 1.1.5 in myPLTW and plot your results.
Well-color data should be recorded (Wednesday); plotted and concentration estimated today.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Distinguish specificity from sensitivity and use your results to discuss how good the test is.
- Write plain definitions: sensitivity catches true positives, specificity avoids false positives.
- Sort your 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 run was and why.
Data table: 2x2 classification table sorting results into true/false positives and negatives, plus a one-sentence reliability judgment citing controls.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Use the checklist just below and upload by 11:29 PM for full credit. Absent with an excused absence? You get two school days for every day you were absent, so this deadline moves with you.
| Task | Who |
|---|---|
| Write plain definitions: sensitivity catches true positives, specificity avoids false positives. | _______ |
| Sort your 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 run was and why. | _______ |
Working solo? Put your own name in "Who" for every row.
- 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.
- 1Do thisDistinguish specificity from sensitivity and use your ELISA results to discuss how good the test is.
- 2Use this resource
- 3Submit thisData table: 2x2 classification table sorting ELISA results into true/false positives and negatives, plus a one-sentence reliability judgment citing controls.
- 4Submit it here
- 1Open the drop folder.
- 2Sign in with your district Microsoft account, not a personal one.
- 3Upload the file, named Lastname_Firstname__Assignment Title.
- 4Your own upload panel says Uploaded with a green check: that is your receipt.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Genetics of Disease (Medical Interventions) › Reading qualitative vs. quantitative color results; false positive/negative risk; control logic. › Data tableOpen the drop folder
Learn it · deck, reading, 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.
works only when steps run in exact order and timing, because each layer must bind before the next so that the final color measures and not error.
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: works only when steps run in exact order and timing, because each layer must bind before the next so that the final color measures and not error.
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.
Sensitivity measures how well a test catches true positives (sick people who test positive); a sensitive test misses few sick people.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
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.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
You will be able to define sensitivity and specificity.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-GEND-2026-09-23 · 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 the lesson page.
- • Choose the strongest supported explanation.
- • Choose the next evidence to collect.
- • Hold the decision because the evidence is insufficient.
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: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Specificity vs sensitivity. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Reason for review: Your team must decide what the evidence from Specificity vs sensitivity supports before submitting the labeled and result claim named on the lesson 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: Sensitivity measures how well a test catches true positives (sick people who test positive); a sensitive test misses few sick people.
- • E2: 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.
- • E3: You will be able to 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).
How to get there: open Clever and sign in with your Microsoft (district) account. Both myPLTW and Schoology are in Clever. Do the activity in myPLTW. Turn the work in on this site or hand it to Mr. Mendoza, because that is the step that counts as submitted. Schoology only shows your report-card grade later.
Check yourself · commit, then reveal▸
Claim ceiling for this check: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Specificity vs sensitivity. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
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.
Go further and get help▸
I can name the procedure's purpose and the evidence I will record. I can identify each named hazard and the control that reduces it: 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. 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.
- SubmittedTurned in the right way, on the class site or handed to Mr. Mendoza in class, and confirmed. Not in Schoology: that is where the report-card grade appears later.
- 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.