Statistics lab analysis
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
Analyze your physiology dataset using descriptive statistics and a comparison test.
- Hand in
- Spreadsheet statistical analysis: summary statistics per condition, t-test result with interpretation, and reproducible step documentation.
- 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 two conditions have different averages. But is that gap big enough to trust, or small enough that random variation between your subjects could have produced it by accident?
Analyze your physiology dataset using and a comparison test.
- • Your analysis reports statistics and a comparison result.
- • You can state whether a difference is statistically meaningful.
- If two groups have averages that differ by 3, what else besides the averages do you need to know before you can say the difference is meaningful?
- Why would writing down each step of your analysis matter to a scientist who has never met you?
- 1Compute summary statistics for each condition in a spreadsheet.
- 2Run or interpret a t-test comparing your conditions.
- 3Determine whether the difference is statistically meaningful.
- 4Record the analysis steps so they are reproducible.
- 5Submit your completed statistical analysis.
What did this day actually feel like?
Statistics lab analysis
LAB Reanalyzing our week six data properly, with the axis honest and the non-significant result reported as non- significant.
Turned in: lab report → Lab Reports 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.
Reanalyzing our week six data properly, with the axis honest and the non-significant result reported as non-significant.
MR. MENDOZA
Nobody lied. Same data, two pictures, opposite claims.
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 difference between averages could come from chance when the data are spread out, so you run a t-test and record every step, because that is what lets you (and anyone else) trust the result is real and repeatable.
- 0-10Open the spreadsheet dataset and verify it matches your submitted
- 10-30Compute summary statistics for each condition: mean, SD, and count
- 30-55Run or interpret a t-test comparing the two conditions; record the result and what it means
- 55-65State whether the difference is statistically meaningful and explain in plain language
- 65-77Record all analysis steps so they are reproducible; submit the completed analysis
- 77-80Exit check: what would change in your conclusion if your had been twice as large?
- • Today you run the full statistical analysis on your physiology dataset.
- • You will use a spreadsheet to compute summary statistics and run or interpret a t-test.
- • Every step must be documented so the analysis can be reproduced -- undocumented analysis is not publishable.
- • Statistical analysis appears directly in the Molecular and Genetic Technology and data-skills strands of WebXam 072125.
- • How to compute summary statistics for each condition in a spreadsheet and interpret what they mean.
- • How to run or interpret a t-test and state whether the result is statistically meaningful.
- • Why recording every analysis step enables someone else to reproduce your result.
Bias, error, graph choice, CER conclusion, limitations. · Statistics lab analysis
Day 3 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 statistics-analysis or data-interpretation activity to review the spreadsheet format and t-test guidance.
Mark the statistics-analysis lab activity complete in your tracker after submitting your analysis.
The graph draft is done; by end of today your full statistical analysis with summary stats, t-test result, and reproducible steps should be submitted.
Spreadsheet with computed summary statistics, t-test result, and reproducible steps recorded, 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. · Statistics lab analysis
Open Problem 2 in your myPLTW course shell and locate the statistics-analysis or data-interpretation activity to review the spreadsheet format and t-test guidance.
The graph draft is done; by end of today your full statistical analysis with summary stats, t-test result, and reproducible steps should be submitted.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Analyze your physiology dataset using and a comparison test.
- Compute summary statistics for each condition in a spreadsheet.
- Run or interpret a t-test comparing your conditions.
- Determine whether the difference is statistically meaningful.
- Record the analysis steps so they are reproducible.
- Submit your completed statistical analysis.
Lab report: Spreadsheet statistical analysis: summary statistics per condition, t-test result with interpretation, and reproducible step documentation.
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 |
|---|---|
| Compute summary statistics for each condition in a spreadsheet. | _______ |
| Run or interpret a t-test comparing your conditions. | _______ |
| Determine whether the difference is statistically meaningful. | _______ |
| Record the analysis steps so they are reproducible. | _______ |
| Submit your completed statistical analysis. | _______ |
Working solo? Put your own name in "Who" for every row.
- Your analysis reports statistics and a comparison result.
- You can state whether a difference is statistically meaningful.
- 1Do thisAnalyze your physiology dataset using descriptive statistics and a comparison test.
- 2Use this resource
- 3Submit thisLab report: Spreadsheet statistical analysis: summary statistics per condition, t-test result with interpretation, and reproducible step documentation.
- 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. › Lab reportOpen 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.
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 difference between averages could come from chance when the data are spread out, so you run a t-test and record every step, because that is what lets you (and anyone else) trust the result is real and repeatable.
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 two conditions have different averages. But is that gap big enough to trust, or small enough that random variation between your subjects could have produced it by accident?
What you already know: 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.
New idea: A difference between averages could come from chance when the data are spread out, so you run a t-test and record every step, because that is what lets you (and anyone else) trust the result is real and repeatable.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Statistics lab analysis. 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 Statistics lab analysis.
- 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 two conditions have different averages. But is that gap big enough to trust, or small enough that random variation between your subjects could have produced it by accident?
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.
How to compute summary statistics for each condition in a spreadsheet and interpret what they mean.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
A difference between averages could come from chance when the data are spread out, so you run a t-test and record every step, because that is what lets you (and anyone else) trust the result is real and repeatable.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
Your analysis reports statistics and a comparison result.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-BFH-2027-03-12 · Simulated classroom evidence scenario
Your role: biomedical design team member
Decision: Your team must decide what the evidence from Statistics lab analysis supports before submitting the lab report 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 lab report.
Claim ceiling: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Statistics lab analysis. 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 Statistics lab analysis supports before submitting the lab report named on the lesson page.
Context: A statistic is only trustworthy if someone else, following your recorded steps, would get the same result. Reproducibility, not a low p-value, is what makes a finding science.
- • T1: Compute summary statistics for each condition in a spreadsheet.
- • T2: Run or interpret a t-test comparing your conditions.
- • T3: Determine whether the difference is statistically meaningful.
- • T4: Record the analysis steps so they are reproducible.
- • T5: Submit your completed statistical analysis.
- • E1: How to compute summary statistics for each condition in a spreadsheet and interpret what they mean.
- • E2: A difference between averages could come from chance when the data are spread out, so you run a t-test and record every step, because that is what lets you (and anyone else) trust the result is real and repeatable.
- • E3: Your analysis reports statistics and a comparison result.
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 Statistics lab analysis. 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 that if the two averages are different, the difference is automatically real and worth reporting.. The trap: A gap between two averages is not proof of anything on its own. If the values within each group are spread out widely, that same gap could easily come from chance, which is exactly what a t-test checks, so reporting the difference without the test can announce a discovery that is not there.
Research question: Is the after-activity heart rate significantly higher than resting?
Summary statistics (from my spreadsheet):
- Rest: mean 72.0 bpm, SD 1.6, n = 5
- Activity: mean 97.6 bpm, SD 2.7, n = 5
Comparison test: two-sample t-test on rest vs activity means.
Result: t was large and the p-value was about 0.0001, well below 0.05.
Interpretation: Because p is less than 0.05, the difference is statistically meaningful: the after-activity mean is higher by an amount unlikely to be due to chance.
Reproducible steps (so a classmate could repeat it):
1. Enter the five rest and five activity readings in two columns.
2. Use AVERAGE and STDEV on each column.
3. Use the T.TEST function (two-tailed, two-sample) on the two columns.
4. Compare the returned p-value to 0.05.
This model shows the level of evidence and organization needed to complete: Completes the Problem 2 statistics analysis: summary statistics per condition, a t-test result with interpretation, and reproducible step documentation.
- State the question and method.
- Present the observations and data with units.
- Explain the result, limitations, and next investigation.
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 completed statistical analysis spreadsheet 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 Statistics lab analysis. 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 Statistics lab analysis. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Your t-test gives p = 0.02, using the common 0.05 cutoff. In plain words, what does that tell you about the difference between your two conditions?
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.
- 2Compute summary statistics for each condition in a spreadsheet.
- 3Run or interpret a t-test comparing your conditions.
- 4Determine whether the difference is statistically meaningful.
- 5Record the analysis steps so they are reproducible.
- 6Submit your completed statistical analysis.
- 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 the statistics analysis on your spreadsheet dataset: compute summary statistics, run or interpret a t-test, and record reproducible steps with the result.
Khan Academy Statistics and ProbabilityThen submit your Lab report. 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 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.