Dilution and ELISA model submission
Open your materials, follow the steps, then turn in your work.
Submit your standard curve, model ELISA data, and interpretation to close the dilution week.
1. Open your materials
Use the materials named in the first step below. Open lesson resources.
2. Start the work
Finalize your standard-curve graph and label the axes clearly.
Show all 6 required steps
- Finalize your standard-curve graph and label the axes clearly.
- Tabulate your model ELISA colors and the concentration you assigned each well.
- Write a short interpretation naming which samples were positive and how you knew.
- Add one sentence on a source of error in reading colors by eye.
- Submit your graph, data table, and interpretation on the class site.
- Confirm it is turned in and note one thing you want to do more carefully in the wet lab.
Lost your place? Lost your place? Check what is finished: standard-curve graph with labeled axes, then the data table of well colors and assigned concentrations, then your written interpretation. Whatever is missing, do it next, then submit all three on the class site and confirm it turned in.
Check your work before submitting
- You will be able to submit a labeled standard curve and ELISA data.
- You will be able to interpret model results against a curve.
- You will be able to name a source of measurement error.
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
- Standard curve graph, model ELISA data table (colors and concentrations), short interpretation of positive results, and one error sentence.
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.
Find this lesson's Schoology assignments
These are existing assignments for your section. Follow the directions in the assignment you are working on; this list does not add new work. Check Schoology for each deadline.
Link will not open? Open Schoology, choose your course and section, and find the title shown above.
How this lesson connects
Keep using what you learned last class: Keep supplied details separate from conclusions. An illustrative color pattern can help explain a model, but it cannot establish a test result without the required reference information and decision rule. Today: A result travels with its graph, data, and interpretation and openly names its error, so that another scientist can replicate it and know exactly how far to trust it.
Optional: listen or watch a unit review▸
Thinking practice for this week▸
Argument: disagreeing well, and when opinion becomes fact
How do we argue productively when we disagree, and when does a claim become accepted as fact?
An argument is not a fight. It is two or more people testing claims against evidence to get closer to the truth. The best disagreements aim at the strongest version of the other side (steelman it), refute the actual reasoning, and stay about the idea, not the person.
A sound argument and a clash of opinions are different things. Opinions can simply differ and both stand. A scientific argument is settled by evidence: the side with stronger, more reliable evidence and better reasoning should win, and everyone should be willing to update.
So when does an opinion become a fact? In science, a claim becomes accepted not because enough people like it, but when independent evidence keeps supporting it and repeated attempts to disprove it fail. That is consensus, and it is provisional: it holds until better evidence changes it. Truth is not a vote, but agreement among many careful, independent investigations is the best signal we have.
- • Steelmans: it takes on the strongest version of the other side.
- • Targets reasoning and evidence, never the person.
- • Is settled by evidence, not by who is louder or more popular.
- • Stays open: the participants will change their minds if the evidence does.
- • A claim earns the label “fact” through repeated, independent evidence, not a popularity vote.
- • Even strong consensus stays open to revision if better evidence appears.
Take a claim from this course that people might dispute. Write the strongest argument for it and the strongest against it, then say which the evidence supports and what would change your mind.
Need help? Warm-up, timing, and directions▸
💡 Big idea: A result travels with its graph, data, and interpretation and openly names its error, so that another scientist can replicate it and know exactly how far to trust it.
- 0-15 minFinalize the standard-curve graph: check axis labels, units, and best-fit line
- 15-30 minComplete the : well ID, observed color, assigned concentration for every well
- 30-50 minWrite the interpretation paragraph: name positive results and explain the evidence from the curve
- 50-62 minWrite the error sentence: name one specific source of uncertainty in reading colors by eye
- 62-72 minSubmit graph, , and interpretation to the class site
- 72-80 minConfirm all items show as turned in; note one thing to do more carefully in the wet lab next week
- • The goal of any lab exercise is not just to run the procedure; it is to produce a record someone else can evaluate.
- • Today you finalize and submit everything from the dilution week: the , the data, and the interpretation.
- • Next week you run the real wet ; what you notice about your errors today shapes how carefully you work then.
- • Exit goal: all three submission items confirmed as turned in before you leave.
- • A complete submission packages the visual evidence (graph), the raw data (table), and the interpretation together.
- • Naming a source of error is part of good science; it shows you understand the limits of your own data.
- • Reviewing your model data before the wet helps you anticipate where precision matters most.
PLTW connection and today's work
Open the ELISA pre-lab submission in myPLTW for Activity 1.1.5 ELISA and confirm your standard-curve work is ready for next week's wet lab.
Today's stopping point: Standard-curve table should be done (Thursday); full pre-lab packet submitted today.
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.
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.
Keep supplied details separate from conclusions. An illustrative color pattern can help explain a model, but it cannot establish a test result without the required reference information and decision rule.
A result travels with its graph, data, and interpretation and openly names its error, so that another scientist can replicate it and know exactly how far to trust it.
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 know which model wells were positive, but a reviewer who was not in the room does not. How do you package your , your data, and your reasoning so they could replicate and trust your call?
What you already know: Keep supplied details separate from conclusions. An illustrative color pattern can help explain a model, but it cannot establish a test result without the required reference information and decision rule.
New idea: A result travels with its graph, data, and interpretation and openly names its error, so that another scientist can replicate it and know exactly how far to trust it.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Dilution and ELISA model submission. Use it with E1-E3; it is a model or context image, not experimental or patient data. What to notice: Trace the labeled testing, treatment, or biological process and identify where evidence limits the decision.
- 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 know which model wells were positive, but a reviewer who was not in the room does not. How do you package your , your data, and your reasoning so they could replicate and trust your call?
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.
- • : A molecule, often on a germ's surface, that the immune system recognizes as foreign and responds to by making matching antibodies.
- • : A Y-shaped immune with two -binding tips that marks a specific foreign target for destruction or neutralization.
- • : A lab test that uses antibodies linked to a color-producing to detect and measure a specific in a sample.
- • : A stepwise process of repeatedly diluting a sample by the same factor to make a range of lower, known concentrations.
- • : A graph made from samples of known concentration, used to read off the unknown concentration of a test sample from its measured signal.
- • : The specific molecule an acts on, fitting into the enzyme's active site so it can be changed into a product.
- • : A measure of how much light a sample blocks at a given wavelength, used to estimate how concentrated a substance is in a solution.
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.
An uses selective binding and controls to produce a measurable signal, but performance, cross-reactivity, sampling, and the decision threshold limit what a result can establish.
Limit: A classroom result does not establish a clinical diagnosis or an exact target concentration unless the validated and support that use.
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 submit a labeled and data.
Limit: E3 defines the classroom product or success criterion. It is not independent scientific evidence and cannot justify a clinical or causal claim.
PLTW-GEND-2026-09-21 · Simulated classroom evidence scenario
Your role: medical interventions team member
Decision: Your team must decide what the evidence from today's lesson supports before submitting the lab report named on today's page.
- • Wait on a recommendation until the clinic reports how often this test misses sick people and flags healthy ones.
- • Decide whether to use the test by asking which error, or , this clinic can afford.
- • Reject the test until the lab fixes its mistakes, because an accurate test should not give false results.
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: 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 lab report named on today's page.
Context: A result is only as trustworthy as its documentation: the graph, the raw data, and the interpretation have to travel together, and naming your own sources of error is what turns a claim into evidence someone else can check.
- • T1: Finalize your standard-curve graph and label the axes clearly.
- • T2: Tabulate your model colors and the concentration you assigned each well.
- • T3: Write a short interpretation naming which samples were positive and how you knew.
- • T4: Add one sentence on a source of error in reading colors by eye.
- • T5: Submit your graph, , and interpretation on the class site.
- • T6: Confirm it is turned in and note one thing you want to do more carefully in the wet lab.
- • E1: An uses selective binding and controls to produce a measurable signal, but performance, cross-reactivity, sampling, and the decision threshold limit what a result can establish.
- • E2: Define variables, controls, sampling, units, and the analysis plan before interpreting a result; analysis cannot repair biased or inconsistent measurement.
- • E3: You can submit a labeled and data.
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 Dilution and model submission. Trace the labeled testing, treatment, or biological process and identify where evidence limits the decision. 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.
For a linear , y = mx + b. To estimate an unknown concentration, use x = (y - b) / m.
A is y = 0.40x + 0.10. An unknown signal is 0.90. x = (0.90 - 0.10) / 0.40 = 2.0 concentration units.
Signal units belong on y. Concentration units belong on x. Confirm the unknown falls inside the standards before interpreting it.
Use the equation or graph supplied today to estimate one unknown. Show the substitution, concentration unit, and range check.
Students often think Students often think admitting a source of error weakens their result, so they leave it out to look more confident.. The trap: Naming a source of error strengthens a result, because it shows you know the limits of your data and where a reader should be cautious. A result with no stated limits looks less trustworthy to a scientist, not more, because every real measurement has error, and hiding it just makes the reader find it for you.
Interpretation: Well 1 was positive because its color matched the most concentrated standard on my curve, while Well 4 was effectively negative. I knew this by reading each well's color signal across to the best-fit line and down to its concentration.
Source of error: reading colors by eye is subjective, so two people might assign slightly different concentrations to a medium-blue well; a plate reader would reduce this error.
For the wet lab I want to: pipette more carefully so my dilution steps are exactly tenfold.
This model shows the level of evidence and organization needed to complete: A packaged submission with the labeled standard curve, the model ELISA data table, a short interpretation of which samples were positive, and one source of error.
- 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 graph, data table, and interpretation on Schoology.
- 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 Dilution and ELISA model submission. 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 model, dilution, by path:Medical-Interventions/Unit-1_How-to-Fight-Infection/1.1_The-Mystery-Infection; keywords:elisa, , dilution. Score 154. 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 model, dilution, by path:Medical-Interventions/Unit-1_How-to-Fight-Infection/1.1_The-Mystery-Infection; keywords:elisa, . Score 146. 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 model, dilution, by path:Medical-Interventions/Unit-1_How-to-Fight-Infection/1.1_The-Mystery-Infection; keywords:, dilution. Score 142. 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.
A classmate submits an ELISA lab report with a beautiful graph and a data table, but no sentence about sources of error and no written interpretation of which samples were positive. Name two things a reviewer could NOT do with this submission, and why.
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.
- 2Finalize your standard-curve graph and label the axes clearly.
- 3Tabulate your model ELISA colors and the concentration you assigned each well.
- 4Write a short interpretation naming which samples were positive and how you knew.
- 5Add one sentence on a source of error in reading colors by eye.
- 6Submit your graph, data table, and interpretation on the class site.
- 7Confirm it is turned in and note one thing you want to do more carefully in the wet lab.
- 8Record each result in the prepared table before interpreting it. Mark missing, repeated, or invalid results truthfully.
- 9Complete 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.
The bench work needs equipment you do not have at home. Do the thinking half now: read the procedure, write your prediction, and set up your data table so it is ready.
Back in class. Ask Mr. Mendoza for the class data set, or for a bench slot to run it yourself. Do not submit a Lab report with invented numbers.
Class still runs. Complete the online activity above (it's self-guided). Need the concept taught without a teacher? Use this authoritative explainer:
HHMI BioInteractive (preview; use fallback if blocked)- 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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