Bias, error, graph choice
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
Identify sources of bias and error and choose the right graph for your physiology data.
- Hand in
- Draft graph showing the physiology comparison with labeled axes, units, a title, and an annotated bias-and-error note.
- 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.
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?
Identify sources of bias and error and choose the right graph for your physiology data.
- • You can name specific bias and error sources in your study.
- • You can justify your graph choice for the comparison.
- Your study compares two conditions. Should the axis that shows your measured values start at zero, and why does that choice change what the reader sees?
- Name one way the order you tested people (who went first, who was tired) could have nudged your numbers in one direction.
- 1List possible sources of bias and measurement error in your study.
- 2Decide which graph type best shows your comparison.
- 3Draft the graph with labeled axes and units.
- 4Note how bias or error could affect what the graph shows.
- 5Submit your graph draft with a bias-and-error note.
What did this day actually feel like?
Bias, error, graph choice
How the same honest data becomes a different claim depending on axis range, bar versus line, and what you leave out.
He showed us a graph with a truncated y-axis that made a tiny difference look enormous, then the same data drawn properly. Nobody lied and the two pictures say opposite things.
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.
How the same honest data becomes a different claim depending on axis range, bar versus line, and what you leave out.
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: 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.
Bias, error, graph choice, CER conclusion, limitations. · Bias, error, graph choice
Day 2 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 graphing or data-visualization activity to review the graph format requirements.
Mark the graphing activity complete in your tracker after submitting your graph draft.
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.
Draft graph with labeled axes, units, and a bias-and-error annotation note turned in on the class site or in person.
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.
Bias, error, graph choice, CER conclusion, limitations. · Bias, error, graph choice
Open Problem 2 in your myPLTW course shell and locate the graphing or data-visualization activity to review the graph format requirements.
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.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Identify sources of bias and error and choose the right graph for your physiology data.
- 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.
Data table: Draft graph showing the physiology comparison with labeled axes, units, a title, and an annotated bias-and-error note.
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 |
|---|---|
| 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. | _______ |
Working solo? Put your own name in "Who" for every row.
- You can name specific bias and error sources in your study.
- You can justify your graph choice for the comparison.
- 1Do thisIdentify sources of bias and error and choose the right graph for your physiology data.
- 2Use this resource
- 3Submit thisData table: Draft graph showing the physiology comparison with labeled axes, units, a title, and an annotated bias-and-error note.
- 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) › Bias, error, graph choice, CER conclusion, limitations. › 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 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 detective board holds observations, possible explanations, and one next question.
- Which notes are direct observations?
- Which notes are explanations?
- What new evidence would separate the explanations?
Keep observations separate from explanations, then collect the evidence that can distinguish the options.
Biomedical investigations use controlled procedures and validated measurements, not intuition alone.
- • Board notes map to E1-E3.
- • Possible explanations map to the decision options.
- • The next question maps to the evidence-based action.
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 Bias, error, graph choice.
- Organize the observation with a stable evidence ID.
- Apply this rule: Keep observations separate from explanations, then collect the evidence that can distinguish the options.
- 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.
- • bias: A systematic error in how data is collected or interpreted that tilts results in one direction, making conclusions less accurate or unfair.
- • limitation: A weakness or boundary of a study, design, or method that restricts how far its results can be trusted or applied.
- • replication: Repeating a study to see if the result holds; a key test of whether a finding is real.
- • : A result unlikely to be due to chance, often shown by a p-value below a set threshold such as 0.05.
- • evidence: The facts, data, and cases that carry your claim. Opinions are free; evidence costs homework.
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.
The specific bias and measurement-error sources that threaten validity in a physiology study.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
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.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
You can name specific bias and error sources in your study.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader 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 Bias, error, graph choice supports before submitting the labeled 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 Bias, error, graph choice. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
= 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.
Comparison: mean heart rate at rest vs after light activity.
Graph choice: a bar graph, because I am comparing the mean of two categories (rest, activity), not a trend over continuous time. Bars make the two means easy to compare side by side.
Axes: x-axis = Condition (Rest, After activity); y-axis = Mean heart rate (bpm), starting at 0 so the bars are not visually exaggerated. Title: 'Mean Heart Rate by Activity Condition'. I added error bars showing the standard deviation.
Bias-and-error note:
- Possible bias: I tested only one subject, so the result may not represent other people (selection bias).
- Possible measurement error: sensor placement could shift between trials, adding noise. I reduced this by keeping the sensor in the same spot.
- How it shows in the graph: the SD error bars make the measurement spread visible, so a reader can see the difference is larger than the within-group variation.
This model shows the level of evidence and organization needed to complete: Completes the Problem 2 graph-choice step: a drafted graph of the two-condition physiology comparison with labeled axes and an annotated note on bias and measurement error.
- 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 the class site, or hand it to Mr. Mendoza in class 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).
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 Bias, error, graph choice. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
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.
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.
- 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.
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.