Submit data table
Open your materials, follow the steps, then turn in your work.
Submit a complete, organized physiology data table ready for analysis.
1. Open your materials
Use the materials named in the first step below. Open lesson resources.
2. Start the work
Verify every trial and condition is recorded with units.
Show all 5 required steps
- Verify every trial and condition is recorded with units.
- Add summary statistics: mean and standard deviation per condition.
- Check for obvious data-entry errors or outliers.
- Write a one-line note on data reliability.
- Submit the physiology data table.
Lost your place? Lost your place? You should have verified every trial and condition has units (step 1) and added mean and SD per condition (step 2). Pick up by checking for data-entry errors or outliers (step 3), writing your one-line reliability note (step 4), then submit the table.
Check your work before submitting
- Your table is complete, labeled, and includes summary statistics.
- You can flag any reliability concerns in the data.
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
- Complete physiology data table: all trials labeled with conditions and units, summary statistics (mean and SD) per condition, and a one-line reliability assessment.
How to submit and name your file
Use the submission route shown on today's today's page.
In Schoology, open your course and the assignment for this lesson. Attach your file, select Submit, and check that it appears in the submission.
PDF upload helpYou get two school days for every day you were absent, so this deadline moves with you.
How this lesson connects
Keep using what you learned last class: A difference between means only matters relative to the spread, so standard deviation and a t-test are required before you can honestly claim the difference is real. Today: A reader cannot verify raw numbers without labeled conditions, units, and summary statistics, so an unverifiable data table leaves the analysis built on it with no foundation.
Optional: listen or watch a unit review▸
Need help? Warm-up, timing, and directions▸
💡 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.
PLTW connection and today's work
Open Problem 2 in your myPLTW course shell and locate the data-table submission or data-quality checkpoint to review the expected format.
Today's stopping point: The statistics practice is done; today you add summary statistics to the raw table, check for errors, and submit the complete physiology data table as the weekly summative.
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.
Course connection
- Activity 2.1.2 Science and the Media
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 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 research team lays out its question, variables, controls, sampling plan, measurement record, and analysis before deciding what the data support.
- Which variable is changed or compared?
- Which conditions and measurements must stay consistent?
- Which conclusion is inside the study's evidence boundary?
Define variables, controls, sampling, units, and the analysis plan before interpreting a result; analysis cannot repair biased or inconsistent measurement.
A well-organized classroom study can still be limited by , measurement quality, confounding, and the population represented.
- • Question and variable cards map to the study design.
- • Control and measurement cards map to fair, reproducible data collection.
- • The conclusion card maps to a bounded claim supported by the analysis.
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: Define variables, controls, sampling, units, and the analysis plan before interpreting a result; analysis cannot repair biased or inconsistent measurement.
- 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 body's ongoing process of keeping internal conditions like temperature, blood sugar, and pH steady despite changes in the outside environment.
- • physiology: The study of how the body's parts function and work together to keep an organism alive and healthy.
- • biometrics: Measurements of unique body features, such as fingerprints, faces, or eye patterns, used to identify a specific person.
- • 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.
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.
Biomedical evidence supports a conclusion only to the level allowed by the measurement, comparison, controls, source quality, and uncertainty in the investigation.
Limit: The classroom evidence supports the stated learning decision, not a real clinical diagnosis, causal conclusion, or treatment recommendation.
Define variables, controls, sampling, units, and the analysis plan before interpreting a result; analysis cannot repair biased or inconsistent measurement.
Limit: A well-organized classroom study can still be limited by , measurement quality, confounding, and the population represented.
Your table is complete, labeled, and includes summary statistics.
Limit: E3 defines the classroom product or success criterion. It is not independent scientific evidence and cannot justify a 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 today's page.
- • Turn the table in now, since the numbers are typed neatly in rows and nothing looks out of place.
- • Hold any reliability call until you know how an odd reading was taken, since the number cannot show a measurement slip.
- • Submit the table only after each condition shows its measurement trials, its units, and an analysis a reader can check.
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 submit . 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 submit supports before submitting the labeled data table and result claim named on today's 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: Biomedical evidence supports a conclusion only to the level allowed by the measurement, comparison, controls, source quality, and uncertainty in the investigation.
- • E2: Define variables, controls, sampling, units, and the analysis plan before interpreting a result; analysis cannot repair biased or inconsistent measurement.
- • 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 Schoology 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).
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 submit . It cannot prove causation, diagnose a real patient, or justify action outside this room.
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.
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.
- 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.
Go 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.
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.
- 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.
This week
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