Validation plan
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
Design a test plan that defines the metrics needed to validate your prototype.
- Hand in
- Validation plan with prototype claim, two measurable metrics with units and pass/fail thresholds, a control, a test procedure, and one error-reduction step.
- 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.
Your prototype is supposed to do something specific. What exact number, measured against what baseline, would let you honestly say it passed or failed?
Design a that defines the metrics needed to validate your prototype.
- • Your plan names two measurable metrics with pass/fail thresholds.
- • You included a control and one error-reduction step.
- What does the word 'control' mean in an experiment, and why would a test need one?
- Turn this vague claim into a measurable one: 'the device makes people more comfortable.'
- 1State the specific claim your prototype is supposed to satisfy.
- 2Choose two measurable validation metrics with units.
- 3Describe the test procedure, including what counts as a pass or fail.
- 4Identify a control or baseline for comparison.
- 5List one source of error and how you would reduce it.
What did this day actually feel like?
Validation plan
How would we know if it worked? Actual measurable criteria and who we would test with. Mine has to include people with limited grip strength, because testing on my own hands proves nothing.
AT HOME, THE NIGHT BEFORE WED APR 7 MP1 tracker audit Marking period audit with an honest confidence rating.
Turned in: tracker → recorded in Class Records
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.
How would we know if it worked? Actual measurable criteria and who we would test with. Mine has to include people with limited grip strength, because testing on my own hands proves nothing.
ME
My matrix picked the concept I already liked. Then I fixed the weights.
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: You define a metric with units, a control, and a pass/fail line so that a result can be traced to the prototype rather than to chance or wishful thinking.
- 0-5 minWarm-up: what would count as proof that your prototype does what you claim?
- 5-20 minState your prototype claim and choose two measurable metrics with units
- 20-40 minWrite the test procedure with pass/fail thresholds
- 40-55 minAdd a control or baseline and identify one error source
- 55-70 min: can your partner run your test from your written plan alone?
- 70-80 minExit ticket: name your two metrics and their pass thresholds
- • A prototype that works is still just a prototype until you can prove it works with data.
- • Today you'll write the validation plan that turns your design claim into a testable experiment.
- • We'll focus on two things: what you measure and what counts as good enough.
- • This plan is the document your teacher and future engineers would use to run the test.
- • A validation metric must have units and a numeric threshold to be testable.
- • A control gives you a baseline so you know whether the prototype is the cause of any result.
- • Identifying error sources in advance is a Lab SOP expectation.
Literature review, decision matrices, validation metrics, MP1 data inflection. · Validation plan
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 Problem 3 in your myPLTW course shell and navigate to the current activity, then write a validation plan that defines measurable metrics for your prototype.
Add your validation plan to the Problem 3 portfolio.
The is done; by end of today your validation plan with two measurable metrics, a control, and one error-reduction step should be submitted.
Completed validation plan submitted before leaving class as evidence of progress.
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.
Literature review, decision matrices, validation metrics, MP1 data inflection. · Validation plan
Open Problem 3 in your myPLTW course shell and navigate to the current activity, then write a validation plan that defines measurable metrics for your prototype.
The is done; by end of today your validation plan with two measurable metrics, a control, and one error-reduction step should be submitted.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Design a that defines the metrics needed to validate your prototype.
- State the specific claim your prototype is supposed to satisfy.
- Choose two measurable validation metrics with units.
- Describe the test procedure, including what counts as a pass or fail.
- Identify a control or baseline for comparison.
- List one source of error and how you would reduce it.
Pre-lab: Validation plan with prototype claim, two measurable metrics with units and pass/fail thresholds, a control, a test procedure, and one error-reduction step.
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 |
|---|---|
| State the specific claim your prototype is supposed to satisfy. | _______ |
| Choose two measurable validation metrics with units. | _______ |
| Describe the test procedure, including what counts as a pass or fail. | _______ |
| Identify a control or baseline for comparison. | _______ |
| List one source of error and how you would reduce it. | _______ |
Working solo? Put your own name in "Who" for every row.
- Your plan names two measurable metrics with pass/fail thresholds.
- You included a control and one error-reduction step.
- 1Do thisDesign a test plan that defines the metrics needed to validate your prototype.
- 2Use this resource
- 3Submit thisPre-lab: Validation plan with prototype claim, two measurable metrics with units and pass/fail thresholds, a control, a test procedure, and one error-reduction step.
- 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. Biotechnology for Health (Biomedical Innovations) › Literature review, decision matrices, validation metrics, MP1 data inflection. › Pre-labOpen 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.
You weight each criterion by how much it matters so that the winning option is chosen by an auditable score, not by whichever design you liked first.
You define a metric with units, a control, and a pass/fail line so that a result can be traced to the prototype rather than to chance or wishful thinking.
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: Your prototype is supposed to do something specific. What exact number, measured against what baseline, would let you honestly say it passed or failed?
What you already know: You weight each criterion by how much it matters so that the winning option is chosen by an auditable score, not by whichever design you liked first.
New idea: You define a metric with units, a control, and a pass/fail line so that a result can be traced to the prototype rather than to chance or wishful thinking.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Validation plan. Use it with E1-E3; it is a model or context image, not experimental or patient data. What to notice: Trace the labeled system, test, or design relationship and identify which evidence should trigger revision.
- Observe or measure the relevant feature in Validation plan.
- 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: Your prototype is supposed to do something specific. What exact number, measured against what baseline, would let you honestly say it passed or failed?
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 careful summary and analysis of existing published research on a topic, used to find what is known and where gaps remain.
- • : The process where independent experts evaluate a research study for quality and accuracy before it is published.
- • : A scoring table that rates each option against the same set of criteria so the best choice can be picked with evidence rather than guesswork.
- • validation: Confirming through testing and evidence that a method, device, or result actually does what it claims and gives accurate, repeatable outcomes.
- • metric: A standard measurement used to track or compare something, such as growth rate, concentration, or survival, giving data a clear number.
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.
A validation metric must have units and a numeric threshold to be testable.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
You define a metric with units, a control, and a pass/fail line so that a result can be traced to the prototype rather than to chance or wishful thinking.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
Your plan names two measurable metrics with pass/fail thresholds.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-BFH-2027-04-05 · Simulated classroom evidence scenario
Your role: biomedical design team member
Decision: Your team must decide what the evidence from Validation plan supports before submitting the pre-lab readiness record 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 pre-lab readiness record.
Claim ceiling: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Validation plan. 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 Validation plan supports before submitting the pre-lab readiness record named on the lesson page.
Context: Validation needs a measurable metric, a control, and a set pass/fail line, because without all three you cannot show the prototype (and not chance) caused the result.
- • T1: State the specific claim your prototype is supposed to satisfy.
- • T2: Choose two measurable validation metrics with units.
- • T3: Describe the test procedure, including what counts as a pass or fail.
- • T4: Identify a control or baseline for comparison.
- • T5: List one source of error and how you would reduce it.
- • E1: A validation metric must have units and a numeric threshold to be testable.
- • E2: You define a metric with units, a control, and a pass/fail line so that a result can be traced to the prototype rather than to chance or wishful thinking.
- • E3: Your plan names two measurable metrics with pass/fail thresholds.
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 Validation plan. Trace the labeled system, test, or design relationship and identify which evidence should trigger revision. 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.
Mean = sum of values / number of values. Median = middle ordered value. Range = maximum - minimum.
For 2, 4, 4, and 10: mean = 20 / 4 = 5, median = 4, and range = 10 - 2 = 8.
Mean, median, and range keep the measurement unit. Order the values before finding the median.
Calculate the requested summary for today's supplied values, then write what it reveals and what it hides.
- • The solution must address the stated need in Validation plan.
- • 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 often think a metric like 'it works better' or 'patients feel better' is measurable enough to validate a prototype.. The trap: 'Better' is not a metric because it has no units and no threshold, so two people could measure the same test and disagree. The trap is a claim you cannot fail. A real metric has units and a numeric pass line set in advance (for example, 'reduces cited pain from 7 to 4 or lower on a 0-10 scale').
Prototype claim: My reminder device gets users to take medication within 15 minutes of the scheduled time.
Metric 1: On-time dosing rate, in percent. Pass threshold: at least 80 percent of doses taken within 15 minutes of schedule.
Metric 2: Time-to-response, in minutes. Pass threshold: average response under 5 minutes after the alert.
Procedure: Run 10 scheduled reminders per test user over one week. Log whether each dose was taken within the 15-minute window and the response time. A user passes if both thresholds are met.
Control: A baseline week with no reminder device, so I can tell whether the device, not memory alone, caused any improvement.
Error source and reduction: Users might forget to log their own doses (recording error). I reduce this by having the device auto-timestamp each acknowledged alert instead of relying on a paper log.
This model shows the level of evidence and organization needed to complete: Completes the Problem 3 validation-plan step: a testable plan with the prototype claim, two measurable metrics with units and pass/fail thresholds, a control, a procedure, and one error-reduction step.
- Identify the purpose, hazards, and required controls.
- Write the procedure in a usable order.
- Confirm materials, measurements, and waste handling before starting.
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: Submit the validation plan on the class site before the end of class.
- 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 Validation plan. 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.
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Placement rationale
Matched Prototype validation and evidence audit by path:Biomedical-Innovations/Problem-3_Medical-Innovation/3.1_Medical-Innovation; keywords:rubric. Score 134. 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 Prototype validation and evidence audit by path:Biomedical-Innovations/Problem-3_Medical-Innovation/3.1_Medical-Innovation; keywords:rubric. Score 130. 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 Prototype validation and evidence audit by path:Biomedical-Innovations/Problem-2_Human-Physiology/2.1_Human-Physiology. Score 126. 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 Validation plan. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
A student writes this validation metric: 'The prototype should make the patient healthier.' What is wrong with it, and how would you rewrite it so it is actually testable?
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.
- 2State the specific claim your prototype is supposed to satisfy.
- 3Choose two measurable validation metrics with units.
- 4Describe the test procedure, including what counts as a pass or fail.
- 5Identify a control or baseline for comparison.
- 6List one source of error and how you would reduce it.
- 7Record each result in the prepared table before interpreting it. Mark missing, repeated, or invalid results truthfully.
- 8Complete 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.
Complete a simulated validation run: enter sample readings into the provided test-plan template and compute whether the prototype passes each metric.
PhET: simulation-based testingThen submit your Pre-lab. 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.
Class still runs. Complete the online activity above (it's self-guided). Need the concept taught without a teacher? Use this authoritative explainer:
Khan Academy: scientific method and experiment designYou've passed Unit 2, so the optional extra-credit track is open. Complete reserved-unit work from home, including virtual labs, for extra credit. Each item shows its correct submission route.
Open the extra-credit track- 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.