Bias, error, graph choice
Open your materials, follow the steps, then turn in your work.
Identify sources of bias and error and choose the right graph for your physiology data.
1. Open your materials
Use the materials named in the first step below. Open lesson resources.
2. Start the work
List possible sources of bias and measurement error in your study.
Show all 5 required steps
- List possible sources of bias and measurement error in your study.
- Decide which graph type best shows your comparison.
- Draft the graph with labeled axes and units.
- Note how bias or error could affect what the graph shows.
- Submit your graph draft with a bias-and-error note.
Lost your place? Pick up where you stopped: if you have not listed bias and error sources yet, start there; if you have that list but no graph, decide your graph type and draft it with labeled axes and units; if the graph is drawn, add the note explaining how bias or error could shift what it shows, then submit.
Check your work before submitting
- You can name specific bias and error sources in your study.
- You can justify your graph choice for the comparison.
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
- Draft graph showing the physiology comparison with labeled axes, units, a title, and an annotated bias-and-error note.
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 privacy safeguard has to be designed into a study before data collection, because it decides what you are allowed to measure and share, so a physiology analysis built without one cannot be trusted or reused. Today: The graph type encodes a claim about your data, so choosing one that fits a two-condition comparison (and starting the axis fairly) is what lets the real pattern show instead of a manufactured one.
Optional: listen or watch a unit review▸
Need help? Warm-up, timing, and directions▸
💡 Big idea: The graph type encodes a claim about your data, so choosing one that fits a two-condition comparison (and starting the axis fairly) is what lets the real pattern show instead of a manufactured one.
- 0-10Introduce bias versus measurement error: definitions and examples in physiology studies
- 10-30List possible bias and error sources specific to your own study
- 30-50Choose a graph type: bar, line, scatter, or box plot -- justify the choice for your comparison
- 50-65Draft the graph with labeled axes, units, and a title
- 65-77Add a bias-and-error annotation note on the graph and submit
- 77-80Exit check: which bias source is most likely to affect your conclusion and why?
- • Before you run your statistics, you need to know what could have gone wrong with your data.
- • Today you will name the bias and measurement-error sources in your study and choose the graph that best shows your comparison.
- • A graph that hides important variation is a misleading graph -- you will learn to make graphs that reveal what the data actually says.
- • Graph literacy and error analysis appear in the data-analysis skills tested by WebXam 072125.
- • The specific bias and measurement-error sources that threaten validity in a physiology study.
- • How to select the appropriate graph type for a comparison between two conditions.
- • How acknowledged bias or error must appear in any honest data interpretation.
PLTW connection and today's work
Open Problem 2 in your myPLTW course shell and locate the graphing or data-visualization activity to review the graph format requirements.
Today's stopping point: The biometric-privacy CER is done; by end of today your draft graph with labeled axes and a bias-and-error note should be submitted.
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 2.1.3 Making Results Meaningful
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 privacy safeguard has to be designed into a study before data collection, because it decides what you are allowed to measure and share, so a physiology analysis built without one cannot be trusted or reused.
The graph type encodes a claim about your data, so choosing one that fits a two-condition comparison (and starting the axis fairly) is what lets the real pattern show instead of a manufactured one.
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: You measured two conditions (say resting versus post-exercise heart rate). Which graph makes the real difference between them visible, and what bias in how you collected the data could be faking that difference?
What you already know: A privacy safeguard has to be designed into a study before data collection, because it decides what you are allowed to measure and share, so a physiology analysis built without one cannot be trusted or reused.
New idea: The graph type encodes a claim about your data, so choosing one that fits a two-condition comparison (and starting the axis fairly) is what lets the real pattern show instead of a manufactured one.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Bias, error, graph choice. 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 today's lesson.
- 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: You measured two conditions (say resting versus post-exercise heart rate). Which graph makes the real difference between them visible, and what bias in how you collected the data could be faking that difference?
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.
- • 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.
- • limitation: A weakness or boundary of a study, design, or method that restricts how far its results can be trusted or applied.
- • : A result unlikely to be due to chance, often shown by a p-value below a set threshold such as 0.05.
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 planned study connects its question to defined variables, controls, sampling, measurement, and analysis so the resulting data can support a bounded and reproducible conclusion.
Limit: A statistical difference or trend does not automatically establish practical importance, causation, generalizability, or freedom from bias.
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.
You can name specific bias and error sources in your study.
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-2027-03-11 · Simulated classroom evidence scenario
Your role: biomedical design team member
Decision: Your team must decide what the evidence from today's lesson supports before submitting the labeled and result claim named on today's page.
- • Use the line graph the spreadsheet suggested first, because it looks cleanest and still shows the difference between conditions.
- • Ask whether every post-exercise reading came the same number of seconds after stopping, since no one logged that timing.
- • Plot the two conditions as separate bars and name one bias in your collection that could fake the gap.
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 today's lesson. It cannot prove causation, diagnose a real patient, or justify action outside this room.
= final volume / sample volume. New concentration = starting concentration / dilution factor.
Mix 1 mL of sample to a final volume of 10 mL. The is 10. A 100 mg/mL starting sample becomes 10 mg/mL.
Use the same volume units before dividing. Concentration keeps its original concentration unit.
Apply the same setup to one supplied dilution or dose. Show the factor, new value, units, and a reasonableness check.
Students often think Students think picking a graph is decoration, so they choose whichever one looks nicest or whichever the spreadsheet suggests first.. The trap: The graph type is not a style choice. A line graph implies change over a continuous variable like time, and using it for two separate categories invents a trend that is not there, so the wrong graph can manufacture a pattern your data never contained.
1. Decision or claim I am testing: ____
2. Evidence ID and exact observation: ____
3. Second evidence ID and exact observation: ____
4. Rule that connects the evidence to my claim: ____
5. Strongest alternative or tradeoff: ____
6. Limitation or missing evidence that controls my confidence: ____
7. Revision I would make if the missing evidence changed: ____
This model shows the level of evidence and organization needed to complete: A blank structure for organizing the assigned response. It contains no claim, ranking, calculation, data interpretation, or recommendation from today's task.
- 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 your graph draft with bias-and-error note on Schoology by end of period.
- 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 Bias, error, graph choice. 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 Statistical analysis and t-test reasoning by path:Biomedical-Innovations/Problem-2_Human-Physiology/2.1_Human-Physiology; keywords:statistical analysis. 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 Statistical analysis and t-test reasoning by path:Biomedical-Innovations/Problem-2_Human-Physiology/2.1_Human-Physiology; keywords:statistical analysis. Score 134. 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 Statistical analysis and t-test reasoning 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).
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 today's lesson. It cannot prove causation, diagnose a real patient, or justify action outside this room.
You are comparing average heart rate in two conditions, rest and after exercise. Is a bar graph or a line graph the honest choice, and where should the value axis start?
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.
- 2List possible sources of bias and measurement error in your study.
- 3Decide which graph type best shows your comparison.
- 4Draft the graph with labeled axes and units.
- 5Note how bias or error could affect what the graph shows.
- 6Submit your graph draft with a bias-and-error note.
- 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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