Mean, SD, t-test
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
Compute the mean and standard deviation and explain the purpose of a t-test for your data.
- Hand in
- Statistics practice: mean and standard deviation calculated for each condition with steps shown, plus a written explanation of what a t-test compares and whether it applies to the data.
- 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 Problem 2 baseline and treatment means differ by a few points, so is that gap bigger than your own trial-to-trial scatter, or could it just be noise?
Compute the mean and and explain the purpose of a t-test for your data.
- • You can compute mean and correctly.
- • You can explain the purpose of a t-test.
- Two datasets have the same average. Name one thing that could still make them very different.
- In one sentence, what do you think is measuring?
- 1Calculate the mean of each condition in your dataset.
- 2Calculate the to describe spread.
- 3Explain what a t-test compares and when to use it.
- 4Identify whether your data would call for a t-test.
- 5Submit your statistics practice with calculations shown.
What did this day actually feel like?
Mean, SD, t-test
Statistics with a purpose. Mean and standard deviation, then a t-test to ask whether two groups actually differ or whether the difference is noise.
Our difference looked convincing in the bar chart and did not survive the test. That was genuinely deflating and completely the point.
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.
Statistics with a purpose. Mean and standard deviation, then a t-test to ask whether two groups actually differ or whether the difference is noise.
ME
It looked convincing in the bar chart and it did not survive the t-test.
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 means only matters relative to the spread, so and a t-test are required before you can honestly claim the difference is real.
- 0-10Review mean and formulas with a small sample dataset
- 10-30Calculate the mean of each condition in your dataset -- show all steps
- 30-50Calculate the for each condition -- show all steps
- 50-65Explain what a t-test compares and when to use it; identify whether your data calls for one
- 65-77Submit statistics practice with calculations shown
- 77-80Exit check: what does a large tell you about your data?
- • Yesterday you collected your data. Today you start making sense of it.
- • Mean and tell you what happened on average and how consistent your trials were.
- • A t-test tells you whether the difference between your two conditions is real or just noise.
- • Statistics fluency (WebXam 072125 data-analysis and Molecular Technology strands) requires you to show your work, not just a number.
- • How to compute the mean and for a dataset by hand or in a spreadsheet.
- • What a t-test compares and the specific circumstances that call for it.
- • Why tells you something about data reliability that the mean alone cannot.
Experimental vs observational studies, sample size, graphing, mean, SD, t-test purpose. · Mean, SD, t-test
Day 4 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 or data-analysis activity to review the calculation format and t-test guidance.
Mark the statistics practice activity complete in your tracker after submitting your work.
The raw is done; by end of today your mean and SD calculations for each condition and a written t-test explanation should be submitted.
Statistics practice submission with mean and calculated for each condition and a written t-test explanation.
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. · Mean, SD, t-test
Open Problem 2 in your myPLTW course shell and locate the statistics or data-analysis activity to review the calculation format and t-test guidance.
The raw is done; by end of today your mean and SD calculations for each condition and a written t-test explanation should be submitted.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Compute the mean and and explain the purpose of a t-test for your data.
- Calculate the mean of each condition in your dataset.
- Calculate the to describe spread.
- Explain what a t-test compares and when to use it.
- Identify whether your data would call for a t-test.
- Submit your statistics practice with calculations shown.
Data table: Statistics practice: mean and calculated for each condition with steps shown, plus a written explanation of what a t-test compares and whether it applies to the data.
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 |
|---|---|
| Calculate the mean of each condition in your dataset. | _______ |
| Calculate the to describe spread. | _______ |
| Explain what a t-test compares and when to use it. | _______ |
| Identify whether your data would call for a t-test. | _______ |
| Submit your statistics practice with calculations shown. | _______ |
Working solo? Put your own name in "Who" for every row.
- You can compute mean and correctly.
- You can explain the purpose of a t-test.
- 1Do thisCompute the mean and standard deviation and explain the purpose of a t-test for your data.
- 2Use this resource
- 3Submit thisData table: Statistics practice: mean and standard deviation calculated for each condition with steps shown, plus a written explanation of what a t-test compares and whether it applies to the data.
- 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.
Single readings hide normal variation, so repeated, calibrated, real-time trials are required for a to count as reproducible scientific evidence.
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 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 Problem 2 baseline and treatment means differ by a few points, so is that gap bigger than your own trial-to-trial scatter, or could it just be noise?
What you already know: Single readings hide normal variation, so repeated, calibrated, real-time trials are required for a to count as reproducible scientific evidence.
New idea: 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.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Mean, SD, t-test. 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 Mean, SD, t-test.
- 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 Problem 2 baseline and treatment means differ by a few points, so is that gap bigger than your own trial-to-trial scatter, or could it just be noise?
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.
How to compute the mean and for a dataset by hand or in a spreadsheet.
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
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.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
You can compute mean and correctly.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-BFH-2027-03-05 · Simulated classroom evidence scenario
Your role: biomedical design team member
Decision: Your team must decide what the evidence from Mean, SD, t-test supports before submitting the labeled and result claim 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 labeled and result claim.
Claim ceiling: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Mean, SD, t-test. 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 Mean, SD, t-test supports before submitting the labeled and result claim named on the lesson page.
Context: The mean and describe your data honestly and a t-test asks whether an observed difference is big enough to trust, so you need both before you are allowed to draw a conclusion.
- • T1: Calculate the mean of each condition in your dataset.
- • T2: Calculate the to describe spread.
- • T3: Explain what a t-test compares and when to use it.
- • T4: Identify whether your data would call for a t-test.
- • T5: Submit your statistics practice with calculations shown.
- • E1: How to compute the mean and for a dataset by hand or in a spreadsheet.
- • E2: 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.
- • E3: You can compute mean and correctly.
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 Mean, SD, t-test. 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 two averages are different, the difference is automatically meaningful and worth reporting.. The trap: A gap between two means can be meaningless, because if the data are widely spread the difference may sit inside normal scatter, so and a t-test are what decide whether the gap is trustworthy, not the means alone.
Using my baseline heart-rate data: 72, 70, 74, 71, 73 bpm.
Mean: (72 + 70 + 74 + 71 + 73) / 5 = 360 / 5 = 72 bpm.
Standard deviation (sample):
- Deviations from mean: 0, -2, +2, -1, +1
- Squared deviations: 0, 4, 4, 1, 1 (sum = 10)
- Divide by n - 1 = 4: 10 / 4 = 2.5
- Square root: SD = about 1.6 bpm
What a t-test compares: a t-test asks whether the difference between the means of two groups (my baseline mean vs my after-activity mean) is large enough, relative to the spread in the data, that it is unlikely to be due to chance alone.
Does my data call for one? Yes. I have two conditions (rest vs activity) and I want to know if the higher activity mean is a real difference, so a two-sample t-test fits.
This model shows the level of evidence and organization needed to complete: Completes the Problem 2 descriptive-statistics practice: mean and standard deviation computed per condition with steps shown, plus a written explanation of when a t-test is used.
- 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 statistics practice with calculations shown 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 Mean, SD, t-test. 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 Mean, SD, t-test. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Condition A mean = 74 (SD = 2). Condition B mean = 70 (SD = 2). A classmate says 'the difference is only 4, so it doesn't matter.' Are they right? Explain using the SD.
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.
- 2Calculate the mean of each condition in your dataset.
- 3Calculate the standard deviation to describe spread.
- 4Explain what a t-test compares and when to use it.
- 5Identify whether your data would call for a t-test.
- 6Submit your statistics practice with calculations shown.
- 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.