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Scientific Method,
Lab Report Format
and Graphing
Observation
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Scientists identify problem to solve
by observing world around them
Ask Questions
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Information collected from
research, observations in attempt
to answer questions
Forming Hypothesis and
Making Predictions
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Hypothesis - statement that can be
tested by observations or
experimentation
– It is a tentative explanation for
problem/question, educated guess
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Prediction - expected outcome of test
–
Setting up a controlled
experiment
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Use controlled experiment to test
hypothesis
– Experiments are planned procedures
to test hypotheses
Record and Analyze Results
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Record data
Put data into graphs
Analyze data
Draw Conclusions
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Does evidence from experiment
support or refute (reject) hypothesis
Publish Results
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Allows others to use information,
repeat experiments to confirm validity
of results, review experimental design
Repeating Investigations
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Experimental results should be able to
be reproduced because nature
behaves in a consistent manner
Theory
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Set of related hypotheses that have
been tested and confirmed many times
by scientists
Controlled Experiments
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Involve a Control group and an
Experimental group
– Control group - all conditions kept the
same, receives no experimental
treatment, is the experimental trial without
the independent variable
–
Controlled Experiments
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Involve a Control group and an
Experimental group
– Experimental group - group that receives
the experimental treatment
–
Variables
Dependent or responding variable - variable
that is measured in an experiment, what
happens because of the independent variable
Controlled Variables (controls) - other factors
that could cause changes in the dependent
variable, so the scientist wants to keep them
the same or constant, so they don’t cause
changes
Controlled Experiments
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Experiment should be repeated (replicates)
or use a large sample size to verify results
Be sure to test only one factor (independent
variable) at a time
Test independent variable at different values
if possible
Writing a Hypothesis
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Often written as an If….then… statement
If (my guess is true) then (I do this, then this should
happen)
If (hypothesis) then (prediction)
If (hypothesis is true) then (independent variable
should have this affect on dependent variable)
If (discuss relationship between independent and
dependent variable) then (I do this to independent
variable, the dependent variable will change in this
way)
Question: Does the amount of light affect how fast a plant
grows?
 Guess: Plants that receive more light will grow faster
 Independent variable = amount of light received
 Dependent variable = increase in growth rate
 Relationship between independent and dependent variable:
Increase in light exposure will cause an increase in growth rate
 Prediction: (what will happen to the experimental group that
receives the independent variable): The group of plants grown in
more than 12 hours of light will show an increase in mass
compared to those grown in less than 12 hours
 If (discuss relationship between independent and dependent
variable) then (I do this to independent variable, the
dependent variable will change in this way)
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Lab Report Format
Before experiment
I.
II.
Purpose: What is the purpose of the
experiment? Why are we doing the
experiment? Background
information, research needed to help
understand or design experiment,
reason leading to hypothesis
(theory)
Materials:
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III. Procedure: Detailed step by step instructions of
exactly what you plan to do. (Can someone else use
your instructions to repeat experiment)
Include diagram of experimental setup
Specifically discuss variables

– Independent – how it will be manipulated, differing
levels/amounts/concentrations to be administered
– Dependent – how it will be measured-tool or instrument to
be used, units, frequency of measurements, if not a
common method of collecting data, a picture or diagram
illustrating how data is to be collected
– Controlled variables specifically how they will be
regulated/controlled if not already done
Safety precautions/equipment required
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IV. Data tables: Blank table to record data.
Prepare before experiment. Think about what
you will measure, how you will measure it, how
long you will measure it, how frequently will
you take measurements, and what instruments
you will use to make the measurements? Units
for data, uncertainties of data
(15-20 measurements)
During experiment
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Collect and record raw data (what you
measured) accurately and neatly into
organized data tables
Data Collection and Processing uncertainties
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For most measuring devices, uncertainty is
half the place value of the last measured
value; ex. 25.5 ºC (± 0.5 ºC)
Rulers have an uncertainty of ±1 of the
smallest division; ex. 3.1cm ( ± 0.1cm)
For electronic instruments the value is ±1
unit of the last decimal place; ex. 13.7 g (±
0.1g)
Data Processing
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Show and perform necessary calculations
(calculate means, standard deviations, rates,
standardize measurements (divide by
volume or surface area to make equivalent)
– Include units, significant figures
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After experiment
V.
Graphs and Charts: graph data or
place in charts to give visual
representation of data. This will help
to analyze data. Choose correct type
of graph to show data, does graph
show data the way that you want it
to?
VI.
Conclusion: Summarize results of
experiment (what happened?).
Analyze results (why it happened?)
– Analyze data and draw conclusions
from results based on reasonable
interpretation of data, referring to
data when possible
– Explain/justify experimental results
–
Evaluating Procedures and Results
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Evaluate weaknesses and limitations of
design of investigation and performance of
your procedure
Focus on systematic errors
Is data reliable, or did these weaknesses
and limitations impact your data
– Small sample size, important variables not
controlled, data not recorded accurately/reliably
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Suggesting improvements
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Suggest realistic improvements to
identified weaknesses and limitations
and should focus on specific pieces of
equipment or techniques used
Error Analysis
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Human error
–
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Systematic errors
– Affects data the same amount every time (equipment not
calibrated, zeroed, worn, procedures incorrect, unreliable)
– Sources usually identifiable, may be eliminated or reduced
by changes to the experiment
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Random error
– Does not affect every measurement taken or affect them in
the same manner (reading of apparatus)
– The more trails done, the less of an effect a random error
may have on results
– May result from limits of accuracy of the apparatus,
inconsistent recording, natural variations in samples
Graphing Data
GRAPHING
1.
2.
Title Graph - short but good descriptive title
that clearly tells what the graph is about.
Identify the Variables
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independent variable goes on X axis
(horizontal) or TIME when the effect of the
independent variable is measured over time
(variable vs. control or different degrees of
variable will be shown as different lines on
graph
3.
4.
Determine the Scale of the Graph determine scale (numerical value for
each square) to best fit the range of
each variable. Spread the graph to
use the MOST of the available space.
Number and Label Each Axis - tells
what data the lines on graph
represent. Include units.
–
5.
Plot the Points
6.
Draw the Graph - connect dots with lines
on continuous data. Show approximate
best fit line/curve if appropriate (most
graphs of experimental data are not drawn
as “connect the dots”
Label Lines or Use Legend - if graph
shows more then one line/set of data, label
line or make a legend/key. Use different
marks/colors for different sets of data
7.
Types of Graphs
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Pie Charts - used to compare parts of a
whole (% of something). Use legend to
describe what each slice represents
Line Graphs - Used for continuous data-data
that is changing. Used to track changes over
time or to measure the effect of one thing on
another
Bar Graph (Histogram) - used to compare
something between groups. Can be used to
show large changes over time.
–
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X-Y plot (Scatterplot) - used to
determine if there is a relationships
between things. Used when data points
are not related/do not show changes
over time/effects
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A normal distribution is a very important
statistical data distribution pattern
occurring in many natural phenomena,
such as height, blood pressure, lengths
of objects produced by machines, etc.
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Normal distributions are symmetrical with a
single central peak at the mean (average) of
the data. The shape of the curve is described
as bell-shaped with the graph falling off
evenly on either side of the mean. Fifty
percent of the distribution lies to the left of the
mean and fifty percent lies to the right of the
mean.
The spread of a normal distribution is
controlled by the standard deviation
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The standard deviation is a statistic that tells
you how tightly all the various examples are
clustered around the mean in a set of data.
When the examples are pretty tightly
bunched together and the bell-shaped curve
is steep, the standard deviation is small.
When the examples are spread apart and the
bell curve is relatively flat, that tells you, you
have a relatively large standard deviation.
The Standard Deviation is a measure of how
spread out numbers are, the average
distance away from the mean
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One standard deviation away from the
mean in either direction on the
horizontal axis accounts for somewhere
around 68 percent of the data.