Experimental design version 4.3
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This document is a comprehensive guide to experimental design aimed at teaching scientific methods and processes through structured activities and assessments. It outlines learning goals, key vocabulary, and detailed steps for planning, conducting, and analyzing experiments, including examples related to plant growth experiments. It emphasizes the importance of controlled variables, data analysis, and evidence-based conclusions in scientific investigations.
![Study Subject (SS): The subject (animal, plant, object etc.) being studied in an investigation.
Variable: Any changed or changing factor used to test a hypothesis or prediction in an investigation that could affect the
results of the investigation. There are 4 types:
• Manipulated Variable (MV): The variable that is changed for the purpose of testing the hypothesis. There’s only one MV.
(Also called the Independent or changed variable.)
• Responding Variable: (RV): The variable being measured to test the Hypothesis. Usually changes in response to changes in
the manipulated variable. There can be more than one RV. (This is also called the Dependent or measured Variable)
• Controlled Variables: (Not MV or RV) = A variable kept the same in an experiment
• Uncontrolled Variables: [that Matter] (Not MV or RV) = Variables that are not controlled & can affect the results of an
experiment (cause error)
Control Trial: The “natural” or “normal” situation. The Control trial data is compared to the Experimental trial data to see if
there is a change caused by the manipulation. (The CT Does not have the MV)
Experimental Trial: The trial(s) containing the manipulated variable, that is/are compared to the control trial(s) to test the
hypothesis. (The ET’s have the MV)
Observation:
a. The skill of recognizing & noting some fact or occurrence in the natural world, includes measuring.
b. A systematic observation is one type of investigation in which data on the study subject is taken
systematically. (regularly)
Data: Quantitative or Qualitative observations recorded from nature or experiments.
Evidence: Observations, measurements, or data that is used to support a scientific conclusion.
Scientific Question: A testable question describing a problem. Includes the manipulated & responding variables and the
study subject.
Predict/Prediction: A statement forecasting a future event or process. (If -> Then format)
Hypothesis: A testable explanation for a specific problem or question. Stated in an If-then-because format predicting a
relationship between two variables (the MV & the RV).
Error: Mistakes of perception, measurement, or process during an investigation; causing an incorrect result or difference in
the data. (Types are: Exp. design, observer, operator, recording, calculation, and tool limitations.)
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Experimental design version 4.3
- 1. A Guide to Experimental Design v4.3 with Science & the Scientific process Reference Guide jschmied©2015 by: John Schmied It is best to download this presentation. This allows users to view & practice with the embedded animations.
- 2. See the companion volume to this presentation! Click the icon below to view
- 3. jschmied©2015 Unit A: Experimental Design Learning Goals Pre-Assessment Activity1:SaveFred! Activity2:PellagraStory WorldofVariables AggressiveBehaviorof Bettas SituationalAnalyses Wall/EdgeSeeking BehaviorofMice ErrorinExperiments Activity8:Diving Submarines Post-Assessment Controlled Experiments 1. I can plan and conduct a controlled experiment. Analyzing Data 1. I am able to correctly analyze data from a scientific investigation.. Use of Evidence to Support Reasoning 1. I am able to use evidence and reasoning to create a proper scientific conclusion. Using Evidence to Support Reasoning 1. I can correctly analyze a scientific scenario Unit Performance Expectations
- 4. Unit A: Experimental Design Level 1 Level 2 Level 3 Level 4 Learning Goal #1: I can plan and conduct a controlled experiment. I can accurately define key vocabulary: Study Subject Manipulated Variable Responding Variable Control Trial Experimental Trial Controlled Variables Observation Data Scientific Question Prediction Hypothesis I can create a proper: Scientific Question Prediction Hypothesis I can identify & set up: Control Trial(s) Experimental Trials I can complete an experiment that: is safe controls variables includes all teammates as equal members answers a scientific question is properly cleaned up & restocked I can explain & show others how to: safely conduct a lab ensure all team members get accurate data. monitor to be sure all team members are able to properly complete a lab. answer a scientific question properly clean & reset a lab. Learning Goal #2: I am able to correctly analyze data from a scientific investigation. I can accurately define key vocabulary: • Error (all types) • Data • Qualitative Measurement • Quantitative Measurement • Reliability • Uncontrolled Variable • Average (mean) I am able to: • Identify errors in data • Identify which data to use as evidence • display data in data tables & graphs • tell if reliability was properly tested for in a lab I can explain how to: • use data as evidence • Identify the type(s) of errors present in data • identify similarities, differences, trends & patterns in my results • tell If an experiment is reliable I can explain to others how to: • use data for evidence for a lab • compare & contrast data from multiple groups to identify sources of error. • control uncontrolled variables that cause error • how to make data more reliable Learning Goal #3: I am able to use evidence and reasoning to create a proper scientific conclusion. I can accurately define key vocabulary: • Conclusion • Infer • Evidence • Reasoning • Trade Offs • Trend • Validity (Challenge) I can: • Identify an experiment’s data • tell what the hypothesis is • write a claim • complete all steps in a conclusion I can use reasons & evidence to: • identify trends & patterns in Control & Experimental Trials. • explain why the data answers the scientific question/hypothesis. • write and support my claim(s). • tell different solutions to a problem using evidence. I am able to explain to others: • how to create an argument that supports a hypothesis • How to modify a model experiment based on test results to improve the design. • I can analyze multiple solutions by evaluating the trade-offs. Learning Goal #4: I can correctly analyze a scientific scenario I can accurately define key vocabulary: • Analyze • Scenario • Variables I can correctly identify • Study subject • Manipulated variable • Responding variable • Controlled variables • Scientific Question I can correctly identify • Control trial(s) • Experimental Trial (s) • Uncontrolled variables • Hypothesis Not Applicable
- 5. Study Subject (SS): The subject (animal, plant, object etc.) being studied in an investigation. Variable: Any changed or changing factor used to test a hypothesis or prediction in an investigation that could affect the results of the investigation. There are 4 types: • Manipulated Variable (MV): The variable that is changed for the purpose of testing the hypothesis. There’s only one MV. (Also called the Independent or changed variable.) • Responding Variable: (RV): The variable being measured to test the Hypothesis. Usually changes in response to changes in the manipulated variable. There can be more than one RV. (This is also called the Dependent or measured Variable) • Controlled Variables: (Not MV or RV) = A variable kept the same in an experiment • Uncontrolled Variables: [that Matter] (Not MV or RV) = Variables that are not controlled & can affect the results of an experiment (cause error) Control Trial: The “natural” or “normal” situation. The Control trial data is compared to the Experimental trial data to see if there is a change caused by the manipulation. (The CT Does not have the MV) Experimental Trial: The trial(s) containing the manipulated variable, that is/are compared to the control trial(s) to test the hypothesis. (The ET’s have the MV) Observation: a. The skill of recognizing & noting some fact or occurrence in the natural world, includes measuring. b. A systematic observation is one type of investigation in which data on the study subject is taken systematically. (regularly) Data: Quantitative or Qualitative observations recorded from nature or experiments. Evidence: Observations, measurements, or data that is used to support a scientific conclusion. Scientific Question: A testable question describing a problem. Includes the manipulated & responding variables and the study subject. Predict/Prediction: A statement forecasting a future event or process. (If -> Then format) Hypothesis: A testable explanation for a specific problem or question. Stated in an If-then-because format predicting a relationship between two variables (the MV & the RV). Error: Mistakes of perception, measurement, or process during an investigation; causing an incorrect result or difference in the data. (Types are: Exp. design, observer, operator, recording, calculation, and tool limitations.) jschmied©2015
- 6. Qualitative measurement: data using descriptive words (e.g., hard/soft, hot/warm/cold) Quantitative measurement: data using numbers (e.g.‚ 42.0 °C , 10.0 sec., 6.0m) Reliability: Practically, we try to get good reliability by repeating our trials multiple times to assure that our data is similar in each trial. Reliability describes the consistency of the results during at least three trials. Average: Commonly called the mean. This is the average of the numbers: a calculated "central" value of a set of numbers. To calculate: Just add up all the numbers, then divide by how many numbers there are. Conclusion: A statement telling the findings of an investigation which explains, with reasons using evidence, why the hypothesis is accepted or rejected. Infer: To arrive at a decision or logical conclusion by reasoning from evidence. An Inference is a logical conclusion based on evidence. Reasoning: the process of forming conclusions, judgments, or inferences from facts or premises. Also the reasons, arguments, proofs, etc., resulting from this process. Trend: a general tendency or movement of data towards a particular answer. (Trending higher…) Validity: A characteristic of an investigation describing the quality of the data collected during an experiment. Strong, or high, validity answers the investigative question with confidence by showing the change in the manipulated variable actually caused a change in the responding variable. This can be done in a couple different ways, usually involving a “check experiment” to see if similar results were obtained. Trade Off: an exchange where you give up one thing in order to get something else that you also desire. Analyze: The definition of analysis is the process of breaking down something into its parts to learn what each does and how each relates to one another. Scenario: a detailed outline of an experiment or situation Investigation: An organized way to study the natural world. Experiment(ing): Testing to determine if a hypothesis is accepted or rejected and WHY. In an experiment one compares the experimental trial(s) to the control trial(s) to identify any differences.. Model: A simple representation of a system. Models are used when studying systems that are too big, too small, or too dangerous to study directly. Modeling can be a form of investigation. System: A set or arrangement of interrelated parts through which matter can cycle and energy or information can flow. jschmied©2015
- 7. The scientific process is relatively easy to understand. You: Develop a question that can be tested. Create a hypothesis. Experiment to see if the hypothesis is accepted. Explain what happened. In practice, it’s just a bit more complicated. The entire process is laid out in detail on the next page. Match up the steps & identify the differences from the overall process with the outline above.
- 8. Identify a Problem 4 Record & Analyze Results / Data 3 Evidence Data Evidence Perform an Experiment Process of testing to see if data from this procedure accepts or rejects the hypothesis. 7 Communicate Results Write up Results a. Peer Review b. Publish c. Defend d. Use information! Hypothesis Rejected? Start over 5 Draw Conclusions Answer the question! Tell if results Accept or Reject hypothesis Discuss data: Hi/Lo range/average Tell: Sources of error Assess reliability Explain how to improve validity Make Observations Make Observations Key Parts of the Scientific Process Create a Testable question jschmied©2015 6 Hypothesis Accepted? Repeat and Recheck Results 3xResults at least 3x
- 9. The Scientific Process: The next part of this learning program goes through the steps of the scientific process using an example experiment. - In this experiment we will test to see if: adding fertilizer to tulips will affect the height of tulip flowers. jschmied©2015
- 10. First step 1. Identify a Problem c. Create a scientific Question a. Decide what to study b. Identify key elements (SS, MV, RV) Here’s an example question: Will tulips grow taller with fertilizer? jschmied©2015 3 Key elements
- 11. b. The Manipulated Variable (MV) Develop a testable question: Identify the Key Variables The variable changed for the purpose of testing the hypothesis. In this case the MV is adding fertilizer to tulips. c. The Responding Variable (MV) The variable being measured to test the Hypothesis. In this case the RV is the height of the tulips a. The Study Subject (SS) The subject (animal, plant, object etc.) being studied in an investigation. In this case the SS is the tulips When you are creating a testable question you’ll need to know:
- 12. Study Subject = Manipulated Variable = Responding Variable = How will adding fertilizer to tulips affect the tulip’s height? How will MV SS RV How will/can… the MV / SS… affect…. the RV? Tulips adding Fertilizer Height of tulips Use this format: Writing a testable question -> Know the SS, MV & RV Example: jschmied©2015
- 13. 2. Form a Hypothesis Hypothesis = Prediction with a reason If, Then – compared to, Because format jschmied©2015
- 14. If fertilizer is applied to tulips, Then the tulips with fertilizer will grow taller Use the If, Then - compared to Prediction format compared to tulips without fertilizer MV SS Exp trial Definite prediction about RV Compare to Control trial Writing the Prediction 2. Form a Hypothesis jschmied©2015
- 15. Because fertilizer has nutrients that increases tulip growth. Therefore tulips with fertilizer will grow taller. Create A Hypothesis => A Prediction with a reason If - Then – Compared to - Because format Because includes SS, MV, RV & specific reasoning If fertilizer is applied to tulips, Then the tulips with fertilizer will increase in height compared to tulips without fertilizer…. Add a reason MV Specific reasoning SS RV 2. Form a Hypothesis Prediction: jschmied©2015
- 16. 3. Perform the Experiment a. Materials d. Procedure b. Trials c. Variables Key elements jschmied©2015
- 17. 3. Perform an Experiment a. Get all Materials jschmied©2015
- 18. 1. What are the Control (CT) & Experimental Trials (ET)? 3. Perform an Experiment: b. Plan the Control & Experimental Trials The RV is measured in both trials The MV jschmied©2015
- 19. Question 2: What are the two types, or groups, of Trials in an Experiment ? 3. Perform an Experiment: b. Plan the Control & Experimental Trials jschmied©2015 The Control Trial = CT The Experimental Trial = ET
- 20. Question 3: What are the key differences between the CT & ET? jschmied©2015 3. Perform an Experiment: b. Plan the Control & Experimental Trials The Control Trial = CT The Experimental Trial = ET 1. The Experimental Trial contains the Manipulated Variable & tests the Hypothesis. 2. The Experimental trial results are compared to the Control Trial to see if there is a difference & if the Hypothesis is accepted.
- 21. c. The World of Variables 3. Perform an Experiment - Identify Key Variables jschmied©2015
- 22. 3. Perform an Experiment Identify Key Variables There’s only one MV in an experiment! There can be more than one RV in an experiment jschmied©2015
- 23. What are two ways to control Variables? c. Controlling Variables 3. Perform an experiment jschmied©2015
- 24. Create a Controlled Environment 3. Perform an Experiment - Controlling Variables One way is to: This method is usually done in a Lab jschmied©2015
- 25. Expose all trials to the same changing conditions. 3. Perform an Experiment - Controlling Variables Another way to control variables is to: This is often called the field methodjschmied©2015
- 26. Identify which is/are: 1. A Controlled variable? 2. The Manipulated variable? 4. The Responding Variable? 3. Uncontrolled Variables? 3. Perform an Experiment – Identify The Key Variables Tulip Height jschmied©2015
- 27. d. Develop a Procedure a. Create list of Materials c. List procedure steps in order Identify when to take data Limit possible sources of error Include: i. Jobs ii. Safety Equipment (PPE) & hazards iii. Clean Up 3. Perform an Experiment b. Develop A Data Table jschmied©2015
- 28. 3. Perform an Experiment – Its only as good as the data gathered. Week 1 jschmied©2015
- 29. 3. Perform an Experiment Be consistent throughout the experiment. Week Three jschmied©2015
- 30. Ensure Reliability: Repeat the experiment multiple times (at least 3) to assure the data is similar. 3. Perform an Experiment jschmied©2015
- 31. 4. Analyze the Data • Calculate Highs, Lows, Averages (ex: ET = Low 20.5 High 34.5) • Compare Experimental data to Control data (ex: The ET grew 14 cm!) • Look for Key Differences (ex: The ET grew faster, yet slowed @ week 4.) • Identify and Interpret patterns & variations (ET is about 2x CT) • Make inferences from the data. (All tulips grow taller with fertilizer.) • Identify possible sources of error. (How often/much watering was done?) The Exp. trial Is growing faster than the Control trial up to week 4. Then growth slows to just about the same as the Control. The Exp. trial Is growing taller & faster than the Control trial. jschmied©2015
- 32. An error is a mistake in perception, measurement or a process. Key types of error are: a. Experimental Design error: b. Operator Error. c. Observation Error: d. Recording Error: e. Calculation Error: f. Measuring tool limitation. jschmied©2015 4. Analyze the Data: Sources of Error: What is error? (See this prezi.)
- 33. a. Restate the question b. Restate the Hypothesis & tell if it was Accepted or Rejected i. Explain why using evidence (Avg, Diffs, Hi, Lows etc) 5. Develop & Communicate a Conclusion: Basic Format ii. Tell what you conclude from the data iii. Make inferences from the findings Clearly distinguish between the evidence and your explanations. iv. Evaluate the Reliability of the data v. Tell sources of error & effect on results vi. Describe how to increase the Validity. jschmied©2015
- 34. a. Question: This experiment investigated whether adding fertilizer to tulips would affect a tulip’s height. b. Hypothesis: The team thought adding fertilize would cause tulips to grow taller . The data accepts this hypothesis i. Data Table 1, shows tulips with fertilizer grew faster 1.8 cm/wk vs without fertilizer 0.96 cm/wk. • Also, tulips with fertilizer grew taller 14 cm vs 7.7 cm with fertilizer over the 8 weeks of trials. • Graph 1 shows that the fertilized tulips grew faster for 4 weeks, then grew a little faster than the non fertilzed tulips for the last 4 weeks. ii. As a result, I conclude that using fertilizer will make tulips grow faster and taller. iii. Infer: Finally, I think fertilizer will make all types of tulips and other bulbs grow faster and taller than without fertilizer. However, there is no data to support that fertilizer will make tulips bloom more or longer. jschmied©2015 5. Develop & Communicate a Conclusion: An EXAMPLE
- 35. 5. Develop a Conclusion: Example Format Continued jschmied©2015 iv. Reliability – this experiment had only one trial. To have reliable data the team would need to repeat the data two more times and get similar results. v. Error: There were several sources of error that could of effected the results. • Experimental Design error: The tulips were not protected from slugs. Both trials tulips were partially eaten by slugs. • Operator error: In week 4 the operator only put ½ the amount of fertilizer required on the experimental trial. This likely slowed tulip growth during this period. vi. Validity: The validity of this experiment could of been improved by doing the same experiment with a similar bulb plant, like daffodils.
- 36. Advanced Elements of an experiment jschmied©2015
- 37. Validity: A characteristic of an investigation describing the quality of the data collected during an experiment. • Strong, or high, validity answers the investigative question with confidence by showing the change in the manipulated variable actually caused a change in the responding variable Validity - What is Validity? jschmied©2015
- 38. To improve validity researchers do other types trials to show that a change in the MV actually caused the change in the RV observed in their experiments. Improving Validity Let’s explore a couple ways that might improve the validity of the results from the Tulip experiment. Assume the original class results show the tulips with fertilizer added actually grew taller. jschmied©2015
- 39. Do more trials, each with different amount of fertilizer. Goal: See if an increase in tulip height can be positively linked to adding more fertilizer. This is called finding causality. Validity Example 1 Data Table 1 - Tulip Height Jan 11 20.5 20.5 20.5 20.6 Jan 18 22.6 23.9 24.0 23.9 Jan 25 24.8 30.1 30.2 29.6 Recorder: Joe Observer: Mary Ann all readings in centimeters Date Control 1 ml wk 2 ml /wk 3 ml/wk jschmied©2015
- 40. Daffodil Trials Week 8 Jan 11 20.5 20.5 Jan 18 22.6 23.9 Jan 25 24.8 30.1 Recorder: Joe Observer: Mary Ann all readings in centimeters Date Control Exp Trial Feb 2 25.9 32.3 Feb 9 26.7 33.4 Feb16 27.2 33.9 Feb23 27.8 34.2 Mar 3 28.2 34.5 Data Table 1 Daffodill Height b. Do more trials with another plant, like daffodils. See if adding fertilizer increases daffodil height. Validity Example 2 jschmied©2015
- 41. c. Do Tulip trials with varying concentrations of fertilizer, but add Daffodil trials too. Validity Example 3 Data Table 1 - DaffodilHeight Jan 11 22.6 22.5 22.4 22.6 Jan 18 23.5 24.9 25.0 24.7 Jan 25 25.7 31.3 31.2 30.8 Recorder: Joe Observer: Mary Ann all readings in centimeters Date Control 1 ml wk 2 ml /wk 3 ml/wk Data Table 1 - Tulip Height Jan 11 20.5 20.5 20.5 20.6 Jan 18 22.6 23.9 24.0 23.9 Jan 25 24.8 30.1 30.2 29.6 Recorder: Joe Observer: Mary Ann all readings in centimeters Date Control 1 ml wk 2 ml /wk 3 ml/wk jschmied©2015
- 42. 7. Communicate results to peers & defend. Information becomes part of the world of science. 1. State the Problem Take data Make Inferences from data about a problem. 1b. Create Question Develop question into potential experiment. Identify SS, MV & RV 2a Create Prediction Finalize details of Experiment….. Control & Exp Trials 2b. Form the Hypothesis 3. Do the experiment Gather data 4. Record & Analyze the data 6. Hypothesis accepted repeat 3x 5. Draw Conclusions Tell if Hypothesis was accepted or rejected discuss data and methods Hypothesis Rejected? start over Final Review: Use the Scientific Method Flow diagram to go over the steps of the process jschmied©2015
- 43. About the author: John Schmied has been a secondary science school teacher for 20 years and is involved in developing practical, yet innovative, hands on curriculum for teens. In addition he is a Chemical Hygiene Officer and an Environmental Educator. He has created, developed and manages a 6 acre Environmental Center at his school site. John’s presentations are viewed worldwide & have been in the top 5% of Slideshare for multiple years. During this time John served as the Strategic planner for the Friends of the Hidden River a 501(C)(3) non profit. • Over the past 13 years Friends helped King County, WA design, fund, construct & develop the 14,800 sqft Brightwater Environmental Center in Woodinville WA. • John is the Director & a principal developer of the Ground to Sound STEM Environmental Challenge course, a locally popular cutting edge environmental program that merges, Science, Tech, Art, Multimedia and other disciplines with Leadership studies at the Center Prior to this period John served as a Coast Guard Officer, primarily involved in ice, navigation & search and rescue operations. His specialties are Ship handling, Diving and Oceanographic Operations. John can be contacted via Linked In.