Marine Biology Carbon Dioxide effects on Algae Oxygen Production Assignment | Online Assignment

Create and design a lab experiment about how different levels of carbon dioxide affect algae oxygen production. Has to be specific.

Ignore the last page in the PDF attached, follow the guidelines on the PDF

Design
1. Title
Short statement summarizing what this lab is about
2. Research Question
Although the general aim of the investigation may be given by the teacher, students
must identify a focused problem or specific research question. Unlike the title above
which might be vague, you need to identify in this section a focused research question
that is in the form of an actual question. As well, you should also include a brief
description of your experiment.
3. Background Information
This is information you need to provide pertaining to the experiment. You will
need to refer to various sources to gain the knowledge necessary to write a
hypothesis and carry out a valid experiment.
4. Hypothesis (Prediction)
While a hypothesis is not required and sometimes is not possible in a formal IB IA
(Internal Assessment) Lab, usually in this class you will try to include a hypothesis. If
you include a hypothesis to help focus your research question then it needs to be an
“if….then…” statement that includes the independent and dependent variables. Your
hypothesis should be supported with an explanation and remember, a hypothesis is an
“educated” guess, not a “random” guess and should be supported by your initial
review of resources.
Ex: If the glucose concentration is increased, then the amount of C02 produced will
also increase. This is because glucose is used by yeast to make ATP through cellular
respiration. The more glucose that is available, the faster the rate of cell respiration,
and the more C02 that will be produced. C02 is a product of cell respiration in yeast,
so the more C02 the faster the rate of cell respiration.
5. Variables: (Factors that can be measured or controlled)
You must list out and identify all of the following:
a. Independent Variable: The variable that YOU manipulate (change), and the
result of this manipulation leads to the measurement of the dependent variable
(Make sure that you choose only one independent variable to change. Some
people inadvertently choose more than one.)
b. Dependant Variable: The variable that changes when you change the
independent variable (what you measure).
c. Control Variable (Constant):
1. The control group in an experiment is the group that nothing is done
to. The control is left alone so that the other variables may be
compared to it.
6. Control of Variables
This is not the same as “the control” above! All the aspects of the experiment that
must be kept constant to ensure that the
tests/experiment is valid. There will be several of these not just one! You must be
thorough and think of as many controlled variables as you can. That said, don’t
include controlled variables that aren’t significant. For example if you’re
measuring the growth of yeast then the temperature definitely needs to be
controlled (assuming temperature is not your independent variable) but the same
location is not going to be a significant factor.
Your must explicitly indicate how each controlled variable identified in your
variables section was controlled. If you can’t actively control a variable then your
method should include a means of monitoring it.
7. Materials
You need to have a detailed and thorough list of equipment used in your
investigation Make sure when writing up your equipment list that you include:
• The sizes of glassware such as beakers, flasks, etc.
• The concentration of chemicals (eg hydrochloric acid, 2.0 M).
• The amounts of chemicals (eg magnesium, 0.50 g).
• The range of a thermometer (eg –20°C to 120°C)
• The amount of each solution (eg 200 mL)
8. Method (Procedures)
You will write your own method, I won’t supply one for you to “copy”. Use
“impersonal language”; which means no words such as “I”, “we”, “they”, etc.
Ex: “I weighed out 0.50 g of magnesium and then I added 2.0 cm3 of 2M
hydrochloric acid” should be rewritten as “Add 0.5 g magnesium to 200 cm3 of
2M hydrochloric acid”
Make sure you record the amounts of materials to a sufficient precision in your
method.
Ex: If you weighed 20 g of magnesium using a volumetric pipette this should be
recorded as 20.00 g and not just 20 g.
Why?
Because the scale has a precision of two decimal points
Don’t include “obvious” steps in your methods. Steps such as: “collect and put on
safety equipment” or “clean up bench and return apparatus to the trolley. These
steps would be relevant to pretty much any experiment and so don’t need to be
included.
While writing up your method in “dot point” or numbered form is acceptable, real
scientists actually write their methods in paragraph form – try it.
Sometimes the best thing to do for your method is to draw a diagram (called a protocol
diagram) of the experimental set up and refer to it. That’s fine if you do a diagram that is
of a suitable size and reasonably detailed. Don’t sketch some doodle on the back of your
report two minutes before you’re about to hand it in. See example below.
Protocol Diagram showing “methods:”
When designing a method, ensure that you change the independent variable enough times
to collect enough points for a reasonable line of best fit. A minimum of five data points
is required for any graph and more should be collected if time and materials allow.
Consider whether or not the graph needs many data points or whether more repeating
would be more worthwhile. If the graph has a complex shape e.g. a titration or cooling
curve, then the more points the better. If the graph is linear then repeating the experiment
for each point would be a better way to improve the correlation coefficient than simply
collecting more points (assuming you already have 8 – 10 points).
The method should include sufficient repeats of trials to ensure that random errors are at
least minimized. Don’t leave this for your evaluation as repeating the experiment is a
pretty lame suggestion – doubly so if you should have had it in your method in the first
place. 5X5 Experimental Design is BEST!! (5 forms of independent variable with 5
repeats of an experiment)
II. Data Collection and Processing
1. Raw Data: Qualitative (Observations) & Quantitative (numerical values)
Make sure you record RAW data in tables and include it in your lab report. Raw data is
the actual data measured in your experiment.
Ex: If you are interested in the change in temperature of a reaction, record the
initial and final temperature as seen below:
Initial Temp (°C ± 0.5°C): 18
Final Temp (°C ± 0.5°C): 49
Temp. Change (°C ± 1.0°C): 31
The temperature change is a result of processing the data and cannot be recorded as raw
data. Don’t mix quantitative and qualitative data in the same column if you have both.
2. Processed Data: When you take the data and do something to it, like find averages,
slopes, changes, standard deviations, etc. this is processing the data. In any lab report
Raw Data
Processed Data
you must have both raw and processed data included. Processed data should also be
placed in tables in your lab report and then converted to graphs. Make sure you
process the raw data CORRECTLY! Use headings in tables and graphs where
possible to explain what section or step of the calculation you are doing. (See
example above)
3. Uncertainty:
Uncertainty is not the same as error. A value that is in error is incorrect. It is
not a true measure of reality. A value can be entirely correct but, no matter what,
it can only be measured to a certain limit of precision – to only so many “decimal
places”. This limit of precision is quantified as the uncertainty.
The uncertainty for a piece of apparatus is either printed on the apparatus (ex:
burette, volumetric glassware) or as a general rule of thumb is half the smallest
measurable or cited value. Ex: If a balance can measure to two decimal places
(0.01 g) then the error in a mass reading with this balance is ±0.005 g.
An exception to the uncertainty being half the smallest instrument value is when
using a stopwatch. According to the display you can measure time to the nearest
100th of a second (0.01 s) with our stopwatches. Based on this the uncertainty
would be ±0.005 s – but somehow I doubt your reflexes are this good. A more
reasonable value would be ±0.5 seconds.
In IB Biology, errors bars should also be included in your graphs.
4. Presentation (Tables and Graphs)
You must show graphic representation of your processed data. Also make sure:
• Your data collection must not look the same as anyone else’s!
• The units are included (they should be cited ONCE in brackets in
each column heading. Don’t put the units after each piece of data)
• The uncertainty of the quantitative data and the units of the
uncertainty need to be recorded once in the column heading
• All data in a column must be given to the same number of decimal
places
• Tables and columns need a border with data centered in the
column.
• The table needs to have a suitable and descriptive heading (“Table
1”, “Data Collection” or “Results” are not suitable headings).
• Using Microsoft Excel may be the best choice for creating graphs
III. Conclusion and Evaluation
1. Conclusion: State a conclusion, with justification, based on a reasonable
interpretation of the data:
Ensure that