Submit data table
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
Submit a complete, organized physiology data table ready for analysis.
- Hand in
- Complete physiology data table: all trials labeled with conditions and units, summary statistics (mean and SD) per condition, and a one-line reliability assessment.
- 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.
If someone opened your Problem 2 table cold, could they see every trial, its condition, its units, and spot any outlier, or would your analysis rest on numbers no one can check?
Submit a complete, organized physiology ready for analysis.
- • Your table is complete, labeled, and includes summary statistics.
- • You can flag any reliability concerns in the data.
- Name two things a must show besides the numbers themselves.
- How would you spot a value that was probably a typo before you analyze anything?
- 1Verify every trial and condition is recorded with units.
- 2Add summary statistics: mean and per condition.
- 3Check for obvious data-entry errors or outliers.
- 4Write a one-line note on data reliability.
- 5Submit the physiology .
What did this day actually feel like?
Submit data table
Raw data, statistics, and an honest statement that our result was not significant.
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.
Raw data, statistics, and an honest statement that our result was not significant.
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: A reader cannot verify raw numbers without labeled conditions, units, and summary statistics, so an unverifiable leaves the analysis built on it with no foundation.
- 0-10Review data-table standards: every trial labeled, every value with units, summary stats included
- 10-30Verify the table: check for missing values, unit errors, and obvious outliers
- 30-50Add summary statistics: mean and for each condition in the table
- 50-65Write a one-line data reliability assessment: what could have introduced error?
- 65-77Submit the physiology
- 77-80Exit check: if a classmate had to replicate your study using only your , could they?
- • Today you finalize your raw and add the summary statistics you computed yesterday.
- • A complete, verified is the artifact that everything else -- graphs, statistics, conclusions -- depends on.
- • If there is an error in the table, every analysis inherits it.
- • This is your Friday summative for the data-collection week.
- • What a complete, labeled physiology must contain: trial numbers, conditions, values, units, and summary statistics.
- • How to detect obvious data-entry errors and outliers before analysis begins.
- • What data reliability means and how to write an honest one-line reliability assessment.
Experimental vs observational studies, sample size, graphing, mean, SD, t-test purpose. · Submit
Day 5 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 2 in your myPLTW course shell and locate the data-table submission or data-quality checkpoint to review the expected format.
Mark the data-table submission activity complete in your tracker after submitting.
The statistics practice is done; today you add summary statistics to the raw table, check for errors, and submit the complete physiology as the weekly summative.
Complete, labeled physiology with all trials, conditions, units, summary statistics, and a one-line reliability note.
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.
Experimental vs observational studies, sample size, graphing, mean, SD, t-test purpose. · Submit data table
Open Problem 2 in your myPLTW course shell and locate the data-table submission or data-quality checkpoint to review the expected format.
The statistics practice is done; today you add summary statistics to the raw table, check for errors, and submit the complete physiology as the weekly summative.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Submit a complete, organized physiology ready for analysis.
- Verify every trial and condition is recorded with units.
- Add summary statistics: mean and per condition.
- Check for obvious data-entry errors or outliers.
- Write a one-line note on data reliability.
- Submit the physiology .
Data table: Complete physiology : all trials labeled with conditions and units, summary statistics (mean and SD) per condition, and a one-line reliability assessment.
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 |
|---|---|
| Verify every trial and condition is recorded with units. | _______ |
| Add summary statistics: mean and per condition. | _______ |
| Check for obvious data-entry errors or outliers. | _______ |
| Write a one-line note on data reliability. | _______ |
| Submit the physiology . | _______ |
Working solo? Put your own name in "Who" for every row.
- Your table is complete, labeled, and includes summary statistics.
- You can flag any reliability concerns in the data.
- 1Do thisSubmit a complete, organized physiology data table ready for analysis.
- 2Use this resource
- 3Submit thisData table: Complete physiology data table: all trials labeled with conditions and units, summary statistics (mean and SD) per condition, and a one-line reliability assessment.
- 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) › Experimental vs observational studies, sample size, graphing, mean, SD, t-test purpose. › 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.
A difference between means only matters relative to the spread, so and a t-test are required before you can honestly claim the difference is real.
A reader cannot verify raw numbers without labeled conditions, units, and summary statistics, so an unverifiable leaves the analysis built on it with no foundation.
A bridge prototype is tested against a load limit, cost limit, and user need before revision.
- Which requirement is a criterion?
- Which limit is a constraint?
- What test result should trigger a redesign?
A design improves when evidence is compared with explicit criteria and constraints.
Biomedical designs also require , ethics, and biological validation beyond a physical prototype test.
- • Bridge requirements map to design criteria.
- • Load results map to E1-E3.
- • Revision maps to the next evidence-based iteration.
Driving question: If someone opened your Problem 2 table cold, could they see every trial, its condition, its units, and spot any outlier, or would your analysis rest on numbers no one can check?
What you already know: A difference between means only matters relative to the spread, so and a t-test are required before you can honestly claim the difference is real.
New idea: A reader cannot verify raw numbers without labeled conditions, units, and summary statistics, so an unverifiable leaves the analysis built on it with no foundation.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Submit data table. 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 Submit .
- Organize the observation with a stable evidence ID.
- Apply this rule: A design improves when evidence is compared with explicit criteria and constraints.
- Choose the option the evidence supports and state the limit of the conclusion.
Real biomedical example: If someone opened your Problem 2 table cold, could they see every trial, its condition, its units, and spot any outlier, or would your analysis rest on numbers no one can check?
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.
- • : The number of subjects or observations in a study; larger samples give more reliable results and reduce the role of chance.
- • mean: The average of a set of numbers, found by adding all the values together and dividing by how many values there are.
- • : A number that measures how spread out data values are around the mean; a small value means values cluster tightly, a large value means they scatter.
- • t-test: A statistical test that compares the average values of two groups to judge whether their difference is likely real or just due to chance.
- • validity: How well a test or study actually measures what it claims to, so the conclusions truly reflect reality.
- • reliability: The degree to which a measurement, method, or person produces the same dependable result each time under the same conditions.
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.
What a complete, labeled physiology must contain: trial numbers, conditions, values, units, and summary statistics.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
A reader cannot verify raw numbers without labeled conditions, units, and summary statistics, so an unverifiable leaves the analysis built on it with no foundation.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
Your table is complete, labeled, and includes summary statistics.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-BFH-2027-03-08 · Simulated classroom evidence scenario
Your role: biomedical design team member
Decision: Your team must decide what the evidence from Submit supports before submitting the labeled data table and result claim named on the lesson page.
- • Select the option best supported by E1-E3.
- • Select a reasonable alternative and name the evidence it would require.
- • Delay the claim because the evidence does not distinguish the options.
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 Submit . 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 Submit supports before submitting the labeled data table and result claim named on the lesson page.
Context: A is only as good as its organization, so if a reader cannot verify your raw numbers, conditions, and units, your analysis has no foundation to stand on.
- • T1: Verify every trial and condition is recorded with units.
- • T2: Add summary statistics: mean and per condition.
- • T3: Check for obvious data-entry errors or outliers.
- • T4: Write a one-line note on data reliability.
- • T5: Submit the physiology .
- • E1: What a complete, labeled physiology must contain: trial numbers, conditions, values, units, and summary statistics.
- • E2: A reader cannot verify raw numbers without labeled conditions, units, and summary statistics, so an unverifiable leaves the analysis built on it with no foundation.
- • E3: Your table is complete, labeled, and includes summary statistics.
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 Submit . 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.
Students often think Students think a finished-looking table with tidy numbers is complete and ready for analysis.. The trap: A neat table can still be unusable, because without labeled conditions, units, per-condition summary statistics, and an outlier check, a reader cannot verify or reproduce it, so tidy is not the same as complete.
Final physiology data table (heart rate, bpm):
Baseline rest, trials 1-5: 72, 70, 74, 71, 73 (mean = 72, SD = 1.6)
After stepping, trials 1-5: 96, 99, 94, 101, 98 (mean = 97.6, SD = 2.7)
Error check: I scanned for typos and impossible values. No reading is below 40 or above 200 bpm, and no value is far from its group, so there are no obvious outliers to flag.
Reliability note (one line): The data is reliable because conditions were controlled and the low standard deviations (under 3 bpm) show the repeated readings agreed closely with each other.
| Condition | Mean (bpm) | SD (bpm) | Trials |
|---|---|---|---|
| Baseline rest | 72.0 | 1.6 | 5 |
| After stepping | 97.6 | 2.7 | 5 |
This model shows the level of evidence and organization needed to complete: Completes the Problem 2 finalized dataset: every trial labeled with conditions and units, mean and SD per condition, and an honest one-line reliability assessment.
- 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: Submit the physiology data table on the class site, or hand it to Mr. Mendoza in class before the end of Friday 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 Submit data table. 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 Human physiology data and research design by path:Biomedical-Innovations/Problem-2_Human-Physiology/2.1_Human-Physiology; keywords:physiology, research design. Score 142. 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 Human physiology data and research design by path:Biomedical-Innovations/Problem-2_Human-Physiology/2.1_Human-Physiology; keywords:physiology, research design. Score 142. 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 Human physiology data and research design by path:Biomedical-Innovations/Problem-2_Human-Physiology/00_Problem-Overview; keywords:physiology, research design. 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 Submit . It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
A reader looks at your table and asks, 'Is 140 in this column a real reading or a typo for 40?' What two features of a good table would let them answer without asking you?
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.
- 2Verify every trial and condition is recorded with units.
- 3Add summary statistics: mean and standard deviation per condition.
- 4Check for obvious data-entry errors or outliers.
- 5Write a one-line note on data reliability.
- 6Submit the physiology data table.
- 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.
Today is individual work you can do from home: complete the same target above, then submit your Data table.
Open the drop folderTurn 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 Statistics and Probability- 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.