In depth overview of scientific method and experimental design. Begins with key goals, vocabulary and the big picture of the basic process. The program breaks down the scientific process using tulips as examples. Covers the entire process including scientific question and hypothesis formation, control and experimental trials, variables and controlling variables. In addition, discusses types of error, reliability and validity. Shows example conclusion as well as gives examples for Validity and Reliability. Designed for initial teaching by elementary and middle school teachers as well as a self paced review for Grades 6-12 and ELL students. Version 4.3 includes the companion volume on page 2, the Science and the Scientific Process reference guide (pdf). One must download the presentation to view this detailed document.

1. A Guide to Experimental Design v4.3
with Science & the Scientific process Reference Guide
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by: John Schmied
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& practice with the embedded animations.

2. See the companion
volume to this
presentation!
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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
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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
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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
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29. 3. Perform an Experiment
Be consistent throughout the experiment.
Week Three
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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.