The lab proforma should be used to complete all lab reports (in 12-14pt. font).
Delete all of the instructions in blue & use all of the writing in black as your section headings.
Title:
Data: Copy and paste processed data and graphs here.
EVALUATION AND CONCLUSION
Graphs and Data from Analysis Lab
Copy and paste the Processed data table and graphs from your DCP lab
Conclusion Statement: ( Aspect 1 )
Write in paragraphform. Use of writing skills, complete sentences and flow of ideas will be
important. May use bullet points. CITE DATA CONSTANTLY
a. Answer the research question by identifying the relationship between the
variables.
EXAMPLE SENTENCES TO HELP
Research question - statement: To verify Hooke’ s law by determining a directly
proportional relationship between the effect of the variables force added to a spring on the
displacement that the spring stretches.
EXAMPLE : The graph and data show that as force is added to the spring the displacement that the
spring stretches is directly proportional to the force. The graph shows a linear relationship that
passes approximately through the origin.
b. Use numerical data to describe the trend observed in graph/s (make specific reference to the
appropriate significant figures and precision ).
SOME EXAMPLE SENTENCES TO HELP :
State the slope measurement with uncertainty and units. State the max and min. slope
values with uncertainty. State that this represents the spring constant k from Hooke’s law
F = kx
State that the slope is used to verify that there is a proportional linear relationship between
the variables force and displacement.
If there is no max or min. state that the uncertainty was too small to measure.
c) State whether the hypothesis is supported or refuted (SPECIFICALLY USE THE WORD
SUPPORTED OR REFUTED) by the data collected in this investigation. Cite data (Note:
words like correct/incorrect & true/false should not be used and are too strong to base off of
only one experiment.)
Example Sentence to Help
Hypothesis : As force is added to the spring the stretch displacement of the spring the will
increase proportionally.
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d) Identify the equation that describes the linear relationship with uncertainty, units & the
correct precision for the slope and y-intercept.
Equation from analysis report
y-variable = (slope value ± uncertainty with units) x-variable + (y-intercept value ±
uncertainty with units)
Evaluating Weaknesses and Suggesting Improvements: (Aspect 2)
EXPERIMENTAL WEAKNESSES AND LIMITATIONS
a) Identify potential systematic errors ( GET FROM DCP report), if evident in graph/s (e.g. if
the uncertainty range for the y-intercept does not include the origin when the line/curve of best fit
is expected to pass through it).
State the direction of systematic errors (when a systematic error is present all data points are
shifted up or all data points shifted down). Cite data.
b) Identify sources of random error & discuss whether they had a significant impact on the data
collected ( GET FROM analysis report). Human error, parallax, etc. Cite data.
USE TABLES FOR GUIDELINES ONLY. BE SURE TO DELETE AFTER FINISHING FINAL REPORT
TABLES TO HELP IDENTIFY SYSTEMATIC ERROR , RANDOM ERROR FOR SIGNIFICANCE AND EFFECTS
FOR ASPECT 1 ABOVE. NOTE THAT IMPROVEMENTS WILL BE ADDRESSED LATER IN ASPECT 3.
Table 1: Systematic Errors
Systematic Errors (if present)
Significance and Direction
Improvement for ASPECT 3
Are due to faulty equipment, poor
calibration and/or experimental
technique. A systematic error is
evident when every data point deviates
from the “correct” value by the same
fixed amount, resulting in a random set
of measurements to be spread about a
value that is not the accepted value.
They are reproducible inaccuracies that
are consistently in the same direction.
Hence, once discovered, systematic
errors are predictable. They can often
be detected by repeating the
measurement using a different method
or different apparatus and comparing
the results.
Discussion of significance is important
i.e. by how much were all data point
shifted.
May need to do internet research for better
methods or equipment.
* See next column for examples *
(Number & list in order of
significance.)
Direction of shift of all data points,
up/above or down/below the expected
result (identified when expected result
is known or when curve/line does not
pass through the origin when it should)
Note: more trials will not reduce the effect
of systematic errors.
* [Examples: badly made
equipment/instruments (e.g. slight
movement in the support for a
pendulum or a stopwatch running too
fast or slow); poorly calibrated
instruments; an instrument having a
zero (calibration) error; using a metal
ruler calibrated at 15ºC in a warmer
laboratory and not allowing for thermal
expansion; incorrect method or
procedure used to take the
measurement (e.g. error in temperature
measurement due to poor thermal
contact between the thermometer and
the substance being measured or
measuring the temperature of a small
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test sample resulting the thermometer
significantly heating the sample);
always rounding down by truncating
the last decimal place.] *
* This table can be removed if systematic errors are not evident.
Table 2: Random Errors
Random Errors
Significance and Effect
Improvement for ASPECT 3
Are due to unpredictable variations in
the performance of the instrument, the
operator and/or environmental
conditions. If readings of a
measurement are above and below the
“correct” value with equal probability,
then the errors are random.
Discussion of significance is important
i.e. how much variation in data
occurred as a result of the error.
May need to do internet research for more
precise measuring equipment.
[Examples: human/parallax error
(misreading/difficult measurements);
using a less sensitive instrument when
a more precise instrument that could be
purchased; poorly timed actions e.g.
reaction time when using a stopwatch;
vibrations & air currents in mass
readings; temperature variations (may
also be a control variable); variation in
the thickness of a surface being
measured e.g. thickness of a wire.]
Effect: caused variation between repeat
trials (revealed in raw data table/s & by
the size of error bars in graph/s); or
caused variation about the curve/line of
best fit (revealed in graph/s by the
proximity of data points to the
curve/line of best fit).
Note: you can nearly always specify –
repeat more trials [except when there is a
high level of precision in raw data (& very
small uncertainties in processed data)].
(Number & list in order of
significance.)
EXPERIMENTAL IMPROVEMENTS
Discuss how you can improve all items in experimenta; weaknesses and limitations
cited above
ACCURACY, PRECISION and RELIABILITY
Discuss the accuracy and precision of the data :
Example sentence to get started : The RELIABILITY of the data and procedure can be
seen from the LINEAR CURVE TREND found in the graph.. Cite specific graph and how
the data points are close showing that the accuracy and precision of the raw data is
acceptable. HOWEVER, talk about some slight INACCURACIES in the data as shown
from the graph , outliers, error bars, other. Always use specific numerical data to support
your statements.
End with general statement , in your opinion, if the overall accuracy, precision and
reliability is adequate or not.
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