There
are three aspects to Data Collection
and Processing. You must collect
and process data accurately.
But equally important—you must
present the data so the reader
can easily interpret it. This means
it must be organized and legible.
The best way to present data is by
using data tables.
Data Tables
Give an
identifying, specific title
to each data table. Number tables
consecutively through the report.
Types of Data
Raw data
is the actual data measured. The
term “quantitative data” refers to
numerical measurements of the
variables associated with the
investigation.
Qualitative observations are just as
important as quantitative
measurements! Make sure you take
note of and record the physical
characteristics of substances or
solutions involved in the
experiment, their changes, whether
something is hot or cold, etc. Some
researchers like to organize these
qualitative observations in a
separate data table – intermingling
them with quantitative data is often
confusing and hard to read.
Units
A
measurement without units is
meaningless! When you make
quantitative observations you are
expected to use the appropriate
units. The system of units used is
the International System of Units -
SI units (Système International
d’Unités). In the table below you
are given some of the more common SI
units you will need to use.
The
following example shows different
ways to express the same unit.
Oxygen consumption (millilters
per gram per hour)
Oxygen consumption (ml/g/h)
Oxygen consumption (ml g-1 h-1)
When
showing length, it is acceptable to
use the associated units shown in
the table below.
When
measuring time, it is acceptable to
use minutes, days or hours when the
experiment spans over a significant
period of time.
Uncertainties
All
measurements have uncertainties
and you must indicate them in your
data tables. This is best done by
paying attention to significant
digits, and by using the
‘plus-or-minus” (+/-) notation.
Examples:
Mass of
a penny on a centigram balance:
3.12g (+/- 0.005g)
Temperature using a typical lab
thermometer: 25.5°C (+/- 0.5 °C)
For our
purposes, the accuracy of a
measurement device is one half of
the smallest measurement possible
with the device. So, for example,
the rulers in class measure to the
millimeter (0.1 cm). So, the
ruler’s measurement uncertainty is
+/- 0.05 cm. If you ask, I will
assist you in determining the
accuracy of the lab equipment we
use. Just as for units, in a column
of data you can show the uncertainty
in the column heading and then you
don’t have to keep re-writing if for
every measurement in the table.
Precision
The
number of significant digits should
reflect the precision of the
measurement. There should be no
variation in the precision of raw
data. In other words, the same
number of digits past the decimal
place should be used. For data
derived from processing raw data
(i.e., means), the level of
precision should be consistent with
that of the raw data.
Lab Drawings
Drawing is a very important skill in
biology and is considered a type of
data collections because drawings
help to record data from specimens.
Drawings can highlight the important
features of a specimen. A drawing
is the result of a long period of
observation at different depths of
focus and at different
magnifications. Read
more about lab
drawings here.
This is the part of the report in
which you take your raw data and
transform it into results that
answer (hopefully!) your research
question. Here you will show the
calculations that give you a
numerical result.
Data processing involves, for
example, combining and manipulating
raw data to determine the value of a
physical quantity (such as adding,
subtracting, squaring, dividing),
and taking the average of several
measurements and transforming data
into a form suitable for graphical
representation. It might be that the
data is already in a form suitable
for graphical presentation, for
example, distance traveled by
woodlice against temperature. If the
raw data is represented in this way
and a best-fit line graph is drawn,
the raw data has been processed.
Plotting raw data (without a graph
line) does not constitute processing
data.
The recording and processing of data
may be shown in one table provided
they are clearly distinguishable.
Calculations of Results
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You will often have to show
calculations. Use plenty of
room; make sure they are
clear and legible. Show the
units of measurements
in all calculations.
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Pay attention to significant
digits! Don’t lose accuracy
by carelessly rounding off.
Round only at the end of a
calculation. Do not
truncate. |
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Identical, repetitive
calculations do not have to
be repeated. Show one
sample calculation (labeling
it as such) and then you
don’t have to repeat it for
all the trials, but only
show the results obtained.
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When calculating an average
value from repeated trials,
don’t average the raw data.
Instead, calculate a result
from each trial. Then
average the results from
each trial to get your final
experimental average.
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Descriptive Statistics
Statistics are useful mathematical
tools which are used to analyze
data. For more information about
statistics,
click here.
Students are expected to decide upon
a suitable presentation format
themselves (for example,
spreadsheet, table, graph, chart,
flow diagram, and so on). There
should be clear, unambiguous
headings for calculations, tables or
graphs. Graphs need to have
appropriate scales, labeled axes
with units, and accurately plotted
data points with a suitable best-fit
line or curve (not a scatter graph
with data-point to data-point
connecting lines). Students should
present the data so that all the
stages to the final result can be
followed. Inclusion of metric/SI
units is expected for final derived
quantities, which should be
expressed to the correct number of
significant figures. The
uncertainties associated with the
raw data must be taken into account.
For more
information about graphing,
click here.
