Mean, SD, t-test
Open your materials, follow the steps, then turn in your work.
Compute the mean and standard deviation and explain the purpose of a t-test for your data.
1. Open your materials
Use the materials named in the first step below. Open lesson resources.
2. Start the work
Calculate the mean of each condition in your dataset.
Show all 5 required steps
- Calculate the mean of each condition in your dataset.
- Calculate the standard deviation 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.
Lost your place? Lost your place? You should have each condition's mean calculated (step 1) and the standard deviation done to describe spread (step 2). Pick up by explaining what a t-test compares and when to use it (step 3), then decide whether your own data calls for one (step 4).
Check your work before submitting
- You can compute mean and standard deviation correctly.
- You can explain the purpose of a t-test.
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
- 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.
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: Single readings hide normal variation, so repeated, calibrated, real-time trials are required for a data table to count as reproducible scientific evidence. Today: 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.
Optional: listen or watch a unit review▸
Need help? Warm-up, timing, and directions▸
💡 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.
PLTW connection and today's work
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.
Today's stopping point: The raw data table is done; by end of today your mean and SD calculations for each condition and a written t-test explanation 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 2.1.2 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.
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.
An engineering team tests one prototype feature at a time against a written criterion and records each failure before revising.
- Which criterion is being tested?
- What stays controlled between trials?
- Which result justifies a specific revision?
A useful prototype test links a controlled trial to a measurable criterion and turns the result into a documented revision decision.
A classroom test can reveal a design weakness without establishing clinical , effectiveness, durability, or regulatory readiness.
- • The written criterion maps to the pass condition.
- • Repeated controlled trials map to test evidence.
- • The revision log maps to the next design change and its rationale.
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 today's lesson.
- Organize the observation with a stable evidence ID.
- Apply this rule: A useful prototype test links a controlled trial to a measurable criterion and turns the result into a documented revision decision.
- 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 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.
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.
A useful prototype test links a controlled trial to a measurable criterion and turns the result into a documented revision decision.
Limit: A classroom test can reveal a design weakness without establishing clinical , effectiveness, durability, or regulatory readiness.
You can compute mean and correctly.
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-2027-03-05 · 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.
- • Report the difference as real, since the baseline and treatment means clearly came out to different numbers.
- • Hold the claim until you compute the , since the means alone cannot show whether the gap exceeds scatter.
- • Compare the gap between your two means against the , then use a t-test to judge it.
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.
Reason for review: Your team must decide what the evidence from today's lesson supports before submitting the labeled and result claim named on today's 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: 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.
- • E2: A useful prototype test links a controlled trial to a measurable criterion and turns the result into a documented revision decision.
- • 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 a difference this big would be unlikely if the two conditions really made no difference.
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 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 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).
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.
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.
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.
- 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.
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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