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APES Guidelines for Experimental Research p. 1
 Laboratory Report Format
Refer to the Experimental Organizer/Design Outline to support completion of the Laboratory Report.
A. Title: Use the “The effect of IV on the DV” format.
B. Introduction/Purpose: Use the following questions to write the introduction (one paragraph per bullet):
• WHAT background information does the reader needs to understand and appreciate the experiment (consider IV and
DV)? Cite references in the body of the text [i.e (Author, Date)] and include each source in the Bibliography.
• WHY is this experiment being done (purpose)? Why is the experiment important; who might be interested in the
results?
• HOW do you predict the experiment will turn out? Explain the rationale behind the Hypothesis statement. Cite
references in the body of the text [i.e (Author, Date)] and include each source in the Bibliography.
C. Hypothesis: Use “If IV then DV” format. Be specific: the hypothesis should be able to be clearly supported or refuted
by your experiment.
D. Materials and Procedures:
1. List all materials used.
2. In sequential order, list the experimental set-up procedures.
3. In sequential order, list the data-collection procedures.
4. Identify the independent variable (IV), levels/treatments of the IV, number of trials for each level, quantitative and
qualitative dependent variables (DV), control and constants of the experiment.
E. Data/Results:
Data Tables: Include quantitative and qualitative data tables with all variables, trials, and statistics.
Graphs: Use appropriate graphs with titles and labeled axes.
Laboratory Notebook: Include if appropriate.
F. Discussion: Use the following questions to write the Discussion (one paragraph per bullet):
• WHAT happened? Describe the data collected for each IV level/treatment. Make references to actual data values and
graphs. Discuss data reliability (refer to standard deviation, when available). Identify patterns/trends and unexpected
results (anomalies) in the data.
• WHY did you get the results you did? Compare your findings to other research (in class or literature) and propose
explanations for discrepancies. Cite references in the body of the text [i.e (Author, Date)] and include each source in the
Bibliography.
• HOW could you improve and further the experiment? Make suggestions for design and/or procedural improvements
and make recommendations for further study.
G. Conclusion: Briefly state major findings of the experiment in reference to the experimental purpose. Was the
Hypothesis supported or refuted?
H. Bibliography/References Cited: Follow the standard format.
APES Guidelines for Experimental Research p. 2
Rules For Reference Citations And Bibliographies (American Psychological Association Guidelines
)The References
Cited section is a bibliography of sources used in producing your report. It usually appears at
the end on a separate page, alphabetically by main entry (e.g., author or other first component). Use the
formatting guidelines depending on the source type you used. Formatting guidelines are also available at:
http://www.english.uiuc.edu/cws/wworkshop/writer_resources/citation_styles/apa/apa.htm.
Bibliographies:
Book: no named author:
Title. (date). Place: Publisher.
Book: one author:
Lastname, Initial. (date). Title. Place: Publisher.
Book: same name after first entry:
___. (date). Title. Place: Publisher.
Book: with editor:
Lastname, Initial. (Ed.) (date).Title. Place: Publisher.
Book: with translator:
OriginatorLastname, Initial. (date). Title. (Initial
Lastname, Trans.) Place: Publisher.
Book: corporate author:
CorporateAuthor. (date). Title. Place: Publisher.
Book: two (through six) authors:
1stLastname, Initial, NextLastname, Initial, &
EndLastname, Initial. (date). Title. Place: Publisher.
Book: more than six authors:
Lastname, Initial, et al. (date). Title. Place: Publisher.
Work in an anthology:
Lastname, Initial. (date). Selection. In Initial Lastname
(Ed.) Title. (pp. x-xx) Place: Publisher.
Article in a reference work:
Lastname, Initial. {if known} (date). Selection. In Initial
Lastname (Ed.), Title (Vol. x, pp. x-xx). Place:
Publisher.
Periodical article:
Lastname, Initial (year, Month day). Article.
JournalTitle, pp.x-xx.
or
Lastname, Initial (year, Month day). Article.
JournalTitle xx (issue#), x-xx {pages}.
Government publication:
GovernmentAgency. (date). Title. Place: Publisher.
Map or chart:
Title, [Medium]. (date). Place: Publisher.
Speech:
Lastname, Initial. (year, Month day). Speechtitle. In
Meetingtitle. Place: SponsoringOrganization.
APES Guidelines for Experimental Research p. 3
Interview:
Lastname, Initial, Personal interview, Month day year.
Correspondence:
Lastname, Initial. (date). [Letter to Initial Lastname].
Mass media:
Lastname, Initial (Producer), {if known} & Lastname
(Director). {if known} Title. [Medium]. Place:
Distributor.
Recording:
Lastname, Initial (Function). (date). Recordtitle.
[Medium} Available: Vendor, Place.
Database source:
Lastname, Initial. {if known} (date). PublicationInfo.
DatabaseTitle. [Medium]. Available: Vendor, Place.
CD-ROM source:
Article. (date). ProductTitle. [Medium]. Place:
Publisher.
Electronic source:
Lastname, Initial. (Dateofwork). Title. [Online].
Available email/ftp/telnet/http:address
[dateofaccess].
In-text citations:
Sources are acknowledged in the body of the text as
follows: ... text (Lastname, date).
Two authors (non-quote):
(1stLastname and 2dLastname, date)
More than two authors (non-quote): [note: the first
time, cite all names]
(1stLastname, NextLastname, and EndLastname, date)
then (1stLastname, et al, date)
One or two authors (direct quote):
(lstLastname and 2dLastname, date, p. x)
More than two authors (direct quote): [note: the first
time, cite all names]
(1stLastname, NextLastname, and EndLastname, date,
p. x) then (1stLastname, et al, date, p. x)
APES Guidelines for Experimental Research p. 4
Experimental Organizer Guidelines
All experiments have certain components in common. Throughout the year we will be using what is called a
“Experimental Organizer” to organize and identify the parts of any scientific experiment. This handout will provide
you with useful definitions and a completed example of a Experimental Organizer, to guide you in using this type of
outline. Note: This is different than the Experimental Design (Procedures), section D, of the Laboratory Report Format.
The Experimental Organizer is a tool for organizing an experiment and writing the Experimental Design (Procedures).
Experimental Organizer Definitions
Title (Purpose): a statement that reflects the variables being tested in the experiment. Often written as The Effects of
…(IV)… on the …(DV)
Hypothesis: a prediction about the relationship between the variables that can be tested. Often
written as “if”, “then” statement.
Independent Variable (IV): the variable that is tested (i.e. the one that is purposefully changed; “cause”).
Category of Independent Variable: there are two categories of independent variables.
 continuous – levels of the independent variable that are measurements based on a continuous scale (generally
graphed using a line graph).
 discontinuous – levels of the independent variable that are discrete categories (generally graphed using a bar
graph).
Control: the standard for comparing experimental effects.
Levels of the Independent Variable (Treatments): the modifications of the independent variable for comparing the
experimental effects to the control.
Repeated Trials: the number of experimental repetitions, objects, or organisms tested at each level of the independent
variable.
Dependent Variable (DV): the variable that is measured (i.e. the data collected; “effect”).
Category of Dependent Variable: there are two major categories of data (use both when possible).
 quantitative - measurements based on a continuous scale (numerical)
 qualitative - counts of discrete or discontinuous categories
Constants (C): all factors that remain the same throughout the experiment.
Experimental Organizer Example
Consider the following scenario:
After studying about recycling, members of John’s biology class investigated the effect of various recycled products on
plant height. Because decomposition is necessary for release of nutrients, the group hypothesized that the type of
compost, grass or food, would affect the height of bean plants. Three flats of bean plants (25 plants/flat) were grown
for 5 days. Then the plants were fertilized as follows: (a) Flat A: 450 g of grass compost, (b) Flat B: 450 g of food
compost, and (c) Flat C: no compost. The plants received the same amount of sunlight and water each day. At the end
of 30 days the group recorded the height of the plants (cm).
Title/Purpose (Describe the experiment by using the format, “The effect of ___ on ___”)
The Effect of Different Types of Compost on Bean Plant Height.
Hypothesis (Make specific predictions about the experimental outcome using the format, “If…, then…”)
If grass compost is applied to the bean plants, then the plants will grow taller than plants grown with food compost or
no compost.
Independent Variable I.V. (What you will be testing): Type of Compost
Category of I.V.: (Identify if the I.V. is continuous or discontinuous.): Discontinuous
Levels of I.V. (Treatments)
(2 or more plus the control, which must be identified)
Grass
compost
Food
compost
Number of Trials (# of times each level of I.V. is tested)
25 plants
25 plants
No compost
(Control)
25 plants
Dependent Variable (What you are measuring. Include all appropriate units): Height of plants (cm)
APES Guidelines for Experimental Research p. 5
Category of Data (Identify if the data is qualitative and/or quantitative): Quantitative
Constants (List all conditions which are the same for each trial):
Amount of light, amount of water, amount of compost
APES Guidelines for Experimental Research p. 6
Measuring & Calculating Data Guidelines
Metric System
The metric system is the most commonly used system of measurement in the scientific community. The units for
measuring length, volume, temperature and mass are given in the table below.
Length
Volume
Temperature
Mass
0.0° Celcius (C°) =
1.00 meter (m) =
1.00 liter (L) =
1.00 gram =
freezing point of water
0.001 kilometer (km)
1,000.0 milliliters (mL) =
0.001 kilogram (kg) =
100.0°C
=
100.0 centimeters (cm)=
3
1,000.0 milligrams (mg)
1000.0 cubic centimeters (cm )
boiling point of water
1,000.0 millimeters (mm)=
1,000,000.0 micrometers (um)
Precision of Measurements
The measurement tool you are using determines the precision of your measurement. You can only be as precise as the
tool you use! Estimate to the nearest half unit shown on your measurement tool.
Calculating with Precision
When calculating with measurement values, remember this basic rule: Your answer should be given to the same degree of
precision as the least precise of the values used in your calculation. We use this simplified rule to make sure we use
"significant figures".
EXAMPLES:
a) 1.0 ÷ 3.0 = .3 (NOT .3333333333333) –accurate to the tenths place (round to the nearest
tenth)
b) 4567.62 x 50 = 228380 –accurate to the tens place (round to the nearest ten)
Metric Conversions
The metric system is based on powers of ten. Conversions between the different units within the metric system is
accomplished by dividing or multiplying by a factor of 10. This involves moving the decimal point the appropriate
number of places in the appropriate direction. The quick conversion line below can assist you. Each vertical line
represents a tens place:
Number of units= 1000 100
10
1
.1
.01
.001
kilo- hecto- deka- UNIT: deci-centi- millimeters
Take away zeros
liters
Add zeros
grams
=Number of units
micro-
To remember the prefixes on the "Quick Conversion Line” use the following statement:
Kids Having Donuts Usually Drink Chocolate Milk.
English Conversions
The English system is used exclusively in the United States. Unlike the metric system, the English system is not based
on tens. Conversions between units require using conversion factors and are therefore much more difficult. As long as
the English system continues to be used, conversions between the two systems will be necessary. Use the conversion
factors below to complete the problems that follow.
Length
Volume
Mass
2.54 cm = 1.0 inch
1.0 liter = 1.06 quarts
1.0 meter = 3.28 feet
3.79 liters = 1 gallon (U.S. liquid)
1.0 meter = 1.094 yards
1.609 kilometer = 1.0 mile
1.0 kilogram = 2.20 pounds
APES Guidelines for Experimental Research p. 7
Data Analysis Guidelines
MEASURES OF CENTRAL TENDENCY
Mean
Generally, the mean is the best measure of central tendency for quantitative data and should be used unless there are
outliers that would distort the mean value.
To calculate mean:
1. Add all data values in the set
2. Divide the sum of the data values by the number of data in the set, this value is the mean.
3. Record this value in the appropriate column of the data table.
Mean =
Sum of the data
Number of data in the set
Summary: Mean is the average of the data set.
Median

Median is an appropriate measure of central tendency for quantitative data that is “skewed” and for qualitative data
with ranked categories. The median is the data value that falls in the exact middle of a data set that is ordered from
smallest to largest.
To determine the median:
1. List all data values in numerical order from least to greatest.
2. If the number of data in the set is odd, find the value that falls in the exact middle of the data set, this value is
the median.
3. If the number of data in the set is even, calculate the mean of the two middle data values, this value is the
median.
4. Record this value in the appropriate column of the data table.
1, 4, 4, 7, 8, 8, 9, 10, 42
Median = 8
1, 7, 8, 8, 10, 10, 42 , 52 Median = 8+10 = 9
2
Summary: Median is the middle number in an ordered data set.
Mode
Mode is an appropriate measure of central tendency for qualitative non-ranked data. The mode of a data set is the
data value that occurs most frequently. If no data value in a data set occurs more than once, then we say that the data
set has no mode. If two or more data value appear the most, then both values are considered the mode.
To determine mode:
1. Look over the data set and count how many times each data value occurs.
2. Identify which data value occurs the most, this value is the mode.
3. Record this value in the appropriate column of the data table.
white, white, red, brown, brown, red, white, white
Mode = white
APES Guidelines for Experimental Research p. 8
Summary: Mode is the most common data value in a data set.
APES Guidelines for Experimental Research p. 9
MEASURES OF VARIATION
Standard Deviation
Standard deviation is a calculated value that describes the variation (or spread) of values in a data set. It is calculated
using a formula that compares each piece of data to the mean. It is useful to think of this number as a “plus or minus”
value, where a larger standard deviation indicates that the data are spread further from the mean and thus the mean is
less reliable. In general, a large value of standard deviation indicates less confidence in the data set and its mean,
while a small value of standard deviations suggests greater confidence in the data and its mean.
To calculate standard deviation:
1. Calculate the mean.
2. Calculate the difference between the mean and each data value.
3. Square each difference.
4. Add the squared values together.
5. Divide the sum by the total number of data in the set.
6. The square root of the value calculated in the previous step is the standard deviation.
7. Record standard deviation as a  value in the appropriate column of the data table.
Plant Biomass (g)
Difference between
Biomass and Mean
(g)
Squared Value of
Difference
3
1
1
5
1
1
6
2
4
4
0
0
6
2
4
2
2
4
2
2
4
Mean: 4
Sum: 18
Sum/Total # of Data:
18/7
Square Root of Quotient: √2.5
Standard Deviation: 1.6
Summary: Standard deviation describes how far the majority of the data is from the mean.
To calculate mean and standard deviation using a graphing calculator* (Texas Instruments):
1.
2.
3.
4.
5.
Press the Stat key, then choose Edit and press Enter.
Enter data values in list one (L1).
Press the Stat key again, choose Calc, then choose 1-Var Stats and press Enter.
Choose 2nd, then L1 (the number 1 key) and press Enter.
The x value is the mean and the x value is the standard deviation.
*Any scientific calculator can calculate mean and standard deviation. Refer to the instructional manual for your
scientific calculator for specific instructions.
APES Guidelines for Experimental Research p. 10
APES Guidelines for Experimental Research p. 11
To calculate standard deviation using Microsoft Excel
1.
2.
3.
4.
Create a data table in an Excel spreadsheet complete with all relevant data values and columns headings for
data analysis (ex. mean, std. dev)
Highlight a cell where you want to add a calculation
Choose “Insert” and then “Function…” from the menu
 Note: There is no “Function” to calculate frequency distribution. You will either need to determine frequency
distribution and input the calculated value into the data table OR create a formula in Excel to calculate frequency
distribution.
Select the cells with the appropriate data to be analyzed
 Either select and highlight the cells, or type the cell numbers into the window that appears.
 Note: When calculating standard deviation, do not include the mean value when selecting the data to be analyzed.
Soil type (IV)
Potting Soil (Control)
Marsh
Mt. Tam
Plant 1
Height in cm (DV)
Plant 3
Plant 2
20.5
10.5
15.2
Mean
22.2
12.2
16.3
Std. Dev. (+/-)
25.1
9.5
14.2
22.6
10.7
15.3
2.3
1.4
1.1
Frequency Distribution
When using qualitative data, frequency distribution is a good expression of variation. Frequency distribution is a
decimal that represents the number of times a particular data value occurs for each experimental group. It is calculated
by dividing the number of times a particular data value occurs by the total number of data in the set.
To calculate frequency distribution:
1. Identify and the number of times a particular data value occurs in a data set.
2. Divide this number by the total number of data in the set.
3. Report this value as a decimal.
4. Calculate and record frequency distribution for all data categories in the appropriate column of the data table.
To present frequency distribution in a data table:
The Effect of the Amount of Compost on the Leaf Quality of Bean Plants
Leaf Quality - 30 Days
Trials
Amount of
Compost
Median
1
2
3
4
5
Frequency Distribution
4
3
2
1
0.0 g
4
4
4
4
4
4
1.0
0
0
0
10.0 g
20.0 g
4
3
3
3
4
2
4
4
4
2
4
3
0.8
0.2
0.2
0.4
0
0.4
0
0
30.0 g
40.0 g
2
2
2
1
3
1
2
1
4
1
2
1
.2
0
0.2
0
0.6
0.2
0
0.8
50.0 g
2
3
3
3
2
3
0
0.6
0.4
0
Overall Plant Health
4 = green color, firm, no curled edges
3 = yellow-green color, firm, no curled edges
2 = yellow color, limp, with curled edges
1 = brown color, limp, with curled leaf
APES Guidelines for Experimental Research p. 12
Graphing Guidelines
Types of Graphs
The type of graph used to display data depends largely on the independent variable.
Category of Data for
Graph Type
IV
continuous
line graph
discontinuous
bar graph
General rules for all graphs.
 Always use graph paper when graphing by hand. Use a ruler for all lines. Graph in pencil first, then trace over it
in ink.
 All graphs should be titled by stating the relationship between the independent and dependent variables.
 Label each axis, with appropriate unit in parentheses. The x-axis represents the independent variable, while the yaxis represents the dependent variable.
 If multiple data sets are included on a graph, it must include a key.
The Line/Scatter-Plot Graph
Constructing a line graph or scatter-plot.
• Draw and label the x and y axes of the graph. Indicate the appropriate units for each variable.
• Determine an appropriate scale for each axis. Do this by finding the range of the values to be graphed
(maximum - minimum), divide this range by the number of grids on the graph paper. To display the data
most clearly, increments should increase using even values (i.e. 2, 4, 6, 8, not 1, 3, 5, 7).
• Carefully plot the data.
• Line Graph: Connect the points (straight line) starting with the first data point and ending with the last data
point. Do not start at the origin unless you have that data (0,0).
• Scatter-Plot: If interested in a linear relationship between the IV and DV, then draw a line of best-fit.
The Bar Graph
Constructing a bar graph.
• Draw and label the x and y axes of the graph. Indicate the appropriate units for each variable.
• Evenly space the x-axis with the levels (treatments) of the independent variable. Evenly distribute the values
along the axis, leaving a space between each value.
• Determine the appropriate scale for the y-axis, using the method described for line/scatter-plot graphs above.
Subdivide the y-axis accordingly.
• Draw a vertical bar from the value of the independent variable on the x-axis to the corresponding value of the
dependent variable on the y-axis. Leave space between each bar.
APES Guidelines for Experimental Research p. 13
Graphing Variation
Plotting Standard Deviation (SD)
 Add the SD to each mean or median and draw a thin horizontal line above the point/bar on the graph.
 Subtract the SD from each mean or median and draw a thin horizontal line below the point/bar on the graph.
 Draw a thin vertical line to connect the two thin horizontal lines. See the diagram below.
The Effect of Compost on Plant Height
25
20
Pla nt Height (cm )
15
Mean Plant Heigh t
10
5
0
No Comp ost
Grass Co mpos t
Type of Compost
Plotting Frequency Distribution (FD)
 A separate bar graph or pie chart can be used to show FD.
Foo d Comp ost
APES Guidelines for Experimental Research p. 14
The Effect of Compost on Plant Health
100%
90%
Frequency Distribution
80%
70%
60%
Unhealthy
Healthy
50%
40%
30%
20%
10%
0%
No Compost
Grass Compost
Food Compost
Type of Compost
Note on Graphing: Use common sense. Think about how to show your data to someone who has never seen it before.
Make it as clear as possible.
Computer Graphing
Use the instructions below to enter, analyze, and graph data in Excel.
Graphing in Excel:
1. Create a data table in an Excel spreadsheet complete with all relevant data values
2. Highlight data to appear on the y-axis (the DV). If appropriate, you can also highlight the data for the x-axis
(the IV).
 Note: in many cases, this will be the values in the central tendency column
3. Select the chart wizard (from toolbar) or “Insert”…”Chart” from the menu
 Select the appropriate type of graph (recommended type for bar graphs is Clustered Column) and click ‘Next’.
 Click on the “Series” tab and change ‘series titles’ and ‘category x- axis labels’ if applicable.
 Note: ‘category x axis labels’ represent the levels of the independent variable
 Click on the grid icon next to the window labeled ‘category x-axis labels’ and then simply highlight the
cells containing the levels of the IV on your data table. Click ‘Next’.
 Title the graph in the window labeled ‘Chart Title’
 Label the x- axis by typing the independent variable into the window labeled ‘Category x- axis’. Don’t
forget units!
 Label the y- axis by typing the dependent variable into the window labeled ‘Value y-axis’. Don’t forget
units!
4. Click ‘Next’, and Finish as New Sheet
 Note: Graphs can often better express the difference between data values by changing the scale of the y-axis. To do
this, double click on the y-axis of your graph. Under “scale”, change the minimum value when appropriate.
Graphing Y Error Bars (standard deviation) in Excel:
1. If the graph is a bar graph, double click on one of the bars. If the graph is a line graph, double click on one of
the data points in the graph.
 Note: This may not work if you created a 3 dimensional bar graph. Change the chart type to Clustered Column.
2. Choose tab titled “y error bars” from dialogue box
3. Click on the grid icon next to the window labeled Custom (+). Highlight the cells containing values for
standard deviation on your data table.
4. Click on the grid icon next to the window labeled Custom (-). Highlight the cells containing values for
standard deviation on your data table.
5. Click O.K.
APES Guidelines for Experimental Research p. 15
Laboratory Report Grading Rubric
CATEGORY
INTRODUCTION
4
 Experiment is supported by
important, relevant and
accurately cited background
research on variables,
purpose and predicted
outcomes.
 Information is explained
clearly, accurately and
thoroughly using student’s
own words.
HYPOTHESIS
 Predicted relationship
between variables and
expected outcomes is clear
and based on research.
 Materials are thoroughly
identified.
PROCEDURES
RESULTS
 Procedures for both set-up
and data collection are
exceptionally detailed, clear
and easy to follow.
 Experimental variables and
constants are clearly
identified.
 Raw data, statistical data and
graphs relevant to hypothesis
are accurate.
 Presentation is skilled and
clear, with all necessary
titles/labels.
3
 Experiment is supported by
relevant and cited
background research on
variables, purpose and
predicted outcomes.
 Information is generally
explained accurately using
student’s own words.
 Predicted relationship
between variables and
expected outcomes is
generally stated and based
on research.
 Materials are generally
identified.
2
 Experiment is partially
supported by relevant
and/or cited background
research on variables,
purpose and/or predicted
outcomes.
 Information explained with
partial accuracy.
1
 Experiment is not supported
by background information
on variables, purpose and/or
predicted outcomes.
 Information is generally
inaccurate and/or not clearly
explained.
 Predicted outcomes are
stated and partially on
research.
 Predicted outcomes are
unclear.
 Materials may be only
partially identified.
 Materials are unclear or not
identified.
 Procedures for both set-up
and data collection are
generally detailed and clear.
 Procedures for set-up and/or
data collection may be
partially complete.
 Procedures for set-up and/or
data collection are
unclear/not provided.
 Experimental variables and
constants are generally
identified.
 Experimental variables and
constants may be partially
identified.
 Experimental variables and
constants are incorrect or not
identified.
 Raw data, statistical data and
graphs are generally relevant
to hypothesis.
 Raw data, statistical data and
graphs are partially relevant
to hypothesis.
 Raw data, statistical data and
graphs are incorrect,
incomplete or not included.
 Presentation is generally
clear, with necessary
titles/labels.
 Clarity and accuracy of
presentation is variable.
 Presentation is unclear
and/or incomplete.
APES Guidelines for Experimental Research p. 16
CATEGORY
4
3
2
1
 Appropriate conclusions are
supported by data and
related to the hypothesis.
 Conclusions are generally
supported by data and
related to the hypothesis.
 Conclusions are partially
supported by data and may
be related to the hypothesis.
 Conclusions are
unclear/incomplete and not
supported by data.
 A clear, detailed explanation
for why results occurred is
supported by cited research.
 An explanation for why
results occurred is generally
supported by cited research.
 An explanation for why
results occurred is partially
supported by research.
 An explanation for why
results occurred is
unclear/incomplete or not
included.
 Analysis of possible error,
improvements and ideas for
further study are insightful
and creative.
 Analysis of possible error,
improvements and ideas for
further study are presented
and are generally insightful.
 Analysis of possible error,
improvements and ideas for
further study may be present.
 Additional information
reflecting what was learned
from the experiment is
clearly presented.
 Additional information
reflecting what was learned
from the experiment is
generally presented
CONCLUSION
 A clear, concise summary of
the experiment’s major
outcomes is clearly related to
the hypothesis.
 A summary of the
experiment’s major
outcomes is generally related
to the hypothesis.
 A summary of the
experiment’s major
outcomes may be related to
the hypothesis.
 A summary of the
experiment’s major
outcomes is unclear/not
included.
BIBLIOGRAPHY
 Research includes multiple,
reputable sources and is
properly cited.
 Research includes multiple
cited sources.
 Research includes cited
sources.
 Research does not include
cited sources.
 Written expression is
partially clear and cohesive.
 Written expression is
disorganized/unclear.
 Errors in grammar and
spelling are noticeable.
 Errors in grammar and
spelling are frequent.
 Aspects of the lab report may
be partially completed or
incorrectly formatted.
 Aspects of the lab report are
incomplete and/or lacking
proper formatting.
DISCUSSION
 Written expression is clear,
cohesive and detailed.
WRITTEN
EXPRESSION
 Errors in grammar and
spelling are extremely rare.
 All aspects of the lab report
are completed according to
prescribed format.
 Written expression is
generally clear and cohesive.
 Errors in grammar and
spelling are rare.
 All aspects of the lab report
are generally completed
according to prescribed
format.
 Additional information
reflecting what was learned
from the experiment may be
partially presented.
 Analysis of possible error,
improvements and ideas for
further study are unclear/not
included.
 Additional information
reflecting what was learned
from the experiment is
unclear/not included.
APES Guidelines for Experimental Research p. 17