Conclusion and Evaluation.
Aspect 1: Concluding
NEVER SAY WORDS LIKE PROVEN or CORRECT. A hypothesis can only be supported or not supported.
Does the data support the hypothesis? What data do you have that suggests that the hypothesis is
supported or not supported? Base your conclusion on trends in graphs, comparisons of graphs, R2
values, t-tests etc. Refer to the data in your conclusion. E.g. The trend line in Figure 1 shows that there
is a positive, linear relationship between the temperature in which S.cerevisiae are grown and the rate
of cellular respiration. The correlation coefficient of 0.9 indicates that it is a strong relationship
between….
The t-test indicates that…. (What is the p value? Is it < 0.05 or > 0.05)
Explain your results using biological theory. What biological process is occurring?
Compare your results to reference material. That is, how accurate are your calculations? Can you
explain why you may not be accurate in your evaluation. How close as a percentage did your values
deviate from theoretical values? Can you explain differences or at least suggest possible reasons why
your values differ from the literature?
Aspect 2: Evaluating Procedures and Aspect 3: Improving the Investigation
Procedural weaknesses and limitations
What was the source of the error and how significant is it? Rank them in order of those that had the
greatest impact to the least. You can present it in the following format if you like. Be specific say where
the error was and how much it affected the results.
Good format for Evaluation
Weakness/limitation
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Significance
Improvement
Apart from the control of variables in your procedural weakness include items such as :
Level of treatment. Were there sufficient levels of treatment to determine if a trend is evident? If not
what do you suggest?
Number of trials at each level of treatment. Were there a sufficient number of trials at each level of
treatment to gather sufficient data to answer the question accurately? If not what do you suggest?
Don’t just say things like, “do more”
Precision of the data. Was the data precise? That is, for your repeats at a particular
temperature/concentration etc. was there a low standard deviation as the measurements were very
“close” to each other (indicating precise data) or was there a high standard deviations the
measurements were “widely” spread form each other (indicating a low precision). Where was the data
imprecise, which data point? How could you improve it? Commented upon with specific reference to
the equipment used. Were appropriate measuring instruments used?
Uncertainty of the measurements.
What were the uncertainties of the measurements as a percentage? We decided that <3% very good,
>10% not good, you would need to look at how you make the uncertainties of the measurements less.
Between 3 and 10% a grey area, results need to be treated cautiously and improvements in
measurements need to mentioned.
Don’t forget to mention exactly where the measurements need improving.
Random errors.
i)
In biological investigations, errors can be caused by
a. changes in the material used, or
b. by changes in the conditions under which the experiment is carried out.
c. e.g. the osmotic potential of potato tissue will vary between different potatoes. Different
parts of the same potato will also show variations, but they will probably show a normal
variation that is less than that from samples taken from different potatoes.
ii) Random errors can, therefore, be kept to a minimum by careful selection of material and by
careful control of variables.
e.g. you could use a water bath to reduce the random fluctuations in ambient temperature.
How were random errors minimized?
Human errors (mistakes)
i)
Human errors can occur when tools, instruments or protocols are used or read incorrectly.
a. e.g. A temperature reading from a thermometer in a liquid should be taken after stirring the
liquid and with the bulb of the thermometer still in the liquid.
b. e.g. Thermometers (and other instruments) should be read with the eye level with the liquid
in the thermometer (reading needle) to prevent parallax error.
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ii) Human errors can be systematic, because the experimenter does not know how to use the
apparatus properly, or
iii) They can be random, because the power of concentration of the experimenter is fading. To help
overcome this:
a. Automated measuring, using a data logger system, can be used
b. Alternatively, the experimenter can take a break occasionally.
How were human errors minimized?
Systematic errors
a) Systematic errors can be reduced if equipment is regularly checked or calibrated to ensure that
it is functioning correctly.
a. e.g. a thermometer should be placed in an electronic water bath to check that the
thermostat of the water bath is correctly adjusted.
How were systematic errors minimised? Are you sure each light bulb is actually producing the wattage
printed on the bulb?
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