Practical Work in Biology

1
Practical Work in Biology

• Experimental Design Skills
• Practical Skills
• Presentation
• Interpretation and Evaluation

2
Hypothesis

• An idea which experiments are designed to test.
• A testable statement.
• A statement that connects the independent and
  dependent variables.
• e.g. 1. Light intensity will affect the growth
  rate of plants.
• 2. An increase in temperature will affect
  the rate of enzyme action.
• 3. Humidity will affect the decay rate of
  compost.
• 4. Soil moisture will affect slater movement.

3
Independent Variable

• Sometimes referred to as the experimental
  variable.
• The variable which is deliberately changed.
• Should always be plotted on the x-axis of a graph
• e.g. 1. Light intensity
• 2. Temperature
• 3. Humidity
• 4. Soil moisture

4
Dependent Variable

• The variable which may change as a result of
  changes to the independent variable.
• Plotted on the Y-axis of a graph.
• e.g. 1. Rate of plant growth
• 2. Rate of enzyme action
• 3. Rate of compost decay
• 4. Concentration of product formed
• 5. Number of millipedes in a dark area.

5
Factors Held Constant

• All factors that are kept the same during an
  experiment.
• An experiment can only have one independent
  variable. All other factors must be kept constant
  if the experiment is to be a fair test.
• e.g. 1. temperature, light intensity, amount and
  source of water, type and amount of
  fertiliser, soil type of containers (size,
  shape, nature)
• 2. pH, concentration of enzyme, concentration
  of substrate

6
Resolution

• Resolution refers to the smallest increment
  measurable by the measuring instrument.
• Resolution is a property of the measuring
  instrument.
• It is determined by the number of digits able to
  be read from the measuring instrument.
• Resolution refers to individual measurements.
• e.g. High resolution 0.001g (electronic
  balance)
• Low resolution 0.1g (triple beam
  balance)

7
Presentation

• All observations and measurements need to be
  recorded.
• Construct tables with headings and appropriate
  units.
• Draw graphs which have a title which clearly
  connects the independent and dependent variables.
• e.g. The effect of varying light intensity on
  the growth rate of plants.
• Describe the results
• e.g. As the temperature increased from 00C to
  250C the rate of enzyme action increased from
  250C to 300C the rate remained the same.

8
Characteristics of Graphs

• Axes labeled with units
• An appropriate scale (uniform and using most of
  the axis)
• Accurate plot of points
• Line of best fit

9
Tables

• Note the headings and units for
  the table
• Which piece of data has an
  inconsistent number of significant figures?

10
Graphs

• The class data was plotted
• Note choice of axes, scales, and units.
• Describe which is the most appropriate line.

11
Random Errors

• Random error is caused by any factor that
  randomly affects the measurement of a variable.
• The amount of random error is reflected in the
  amount of scatter in the data.
• An increase in sample size allows averages to be
  calculated. This will tend to reduce the effect
  of random errors.
• Measurements can never be done perfectly and
  therefore random errors can never be eliminated.
• e.g. inconsistent reading of scales,
• inconsistent measuring of volumes in a reaction
  mix,
• inconsistent use of a timer.

12
Systematic Errors

• Systematic errors are present when measured
  values differ consistently from their true value.
• Usually associated with apparatus being faulty or
  incorrectly calibrated, or experimental design.
• Tend to be consistent throughout the experiment
  therefore taking an average does not correct the
  problem or give a more accurate value.

13
Systematic Errors

• Repeating an experiment may identify systematic
  errors.
• In repeating, an alternative source of equipment
  and materials must be used
• e.g. incorrectly calibrated electronic balance,
  or a contaminant in a container or chemical used
  will give an inconsistent result.
• A consistent result indicates that any conclusion
  is likely to be valid.

14
Sample Size

• Refers to the number of samples in the
  experimental group.
• Increasing the number of samples allows averages
  to be calculated.
• Increasing the number of samples will reduce the
  effect of random errors and will therefore make
  the data more consistent and hence reliable.
• e.g. In testing the effect of temperature on
  enzyme action, you might
  decide to have 3 trials (or samples) for each
  temperature.

15
Reliability

• Reliability refers to the extent which an
  experiment yields the same results on repeated
  trials under the same conditions each time.
• Reliability is achieved by minimising the effect
  of random errors.
• e.g. Ensure that a large number of samples is
  used.
• Be attentive and careful when taking and
  recording measurements.

16
Repeating the experiment

• Repeating the experiment with the same procedure
  but different apparatus on different occasions
  helps to identify systematic errors.
• We repeat an experiment to verify our results, to
  check the validity of our experimental design,
  and to be more confident with any conclusions.
• e.g. Repeating a whole experiment on a
  different occasion, preferably with different
  experimenters and different subjects, new
  solutions or equipment etc., to see if results
  are similar.

17
Validity

• Validity refers to the degree to which an
  assessment method measures what it is supposed to
  measure.
• Validity is increased by
• 1. appropriate experimental design (i.e. it
  is testing what it claims to test) and
• 2. repeating the experiment (which reveals
  systematic errors).
• e.g. Consider hypothesis, variables, controlled
  factors, size of sample, apparatus, the control,
  measuring instruments.

18
Relationship Between Precision Accuracy

• systematic

random
High precisionlow accuracy
Low precisionhigh accuracy (fluke)
errors still present
random systematic
High precisionhigh accuracy
Low precisionlow accuracy
19
Relationship between precision and accuracy II
P not A
P and A
Not A not P
A not P
20
Precision

• Precision depends on how well random errors are
  minimised.
• Random errors are present when there is scatter
  in the measured values.
• Scatter therefore influences precision.
• High scatter reflects low precision.
• Low scatter reflects high precision.

21
Accuracy

• Accuracy refers to how close the result of the
  experiment is to the true value.
• Systematic errors need to be detected if the
  result is to be accurate.
• The most likely way to detect systematic errors
  is by repeating the experiment.
• e.g. Recalibrate equipment !

22
Precise or accurate?

• Student A
• pH 4.3
• pH 5.0
• pH 4.9
• pH 4.4
• pH 4.7
• Mean 4.6

• Student B
• pH 4.5
• pH 4.6
• pH 4.6
• pH 4.5
• pH 4.5
• Mean 4.5

23
Resolution and Precision
The resolution of the stopwatch is 0.01 s but the
precision of the data does not match this.
24
Resolution and Precision
The resolution of the stopwatch is now 0.1 s.
25
Interpretation of Data

• Written in the third person (stated objectively).
• Inferences can be made when interpreting the
  data.
• An inference is reasoning based on observation
  and experience. To infer is to arrive at a
  decision or opinion by reasoning from known
  facts.
• e.g. An increase in temperature influenced the
  kinetic energy of molecules, therefore increasing
  the rate of enzyme reaction.

26
Analysis and Evaluation of the Experiment

• Identify sources of, and distinguish between,
  random and systematic errors.
• List ways to improve procedures of the
  experiment.
• Comment on suitability and importance of the
  sample size.
• Comment on the accuracy and precision of the
  results of the experiment.
• Comment on the value of repeating the experiment.

27
Writing a Conclusion

• A conclusion is a brief statement related to the
  initial hypothesis.
• It should be written at the end of each
  experiment.
• A conclusion supports or refutes the hypothesis.
• Experiments DO NOT PROVE hypotheses.
• Confidence in the validity of a conclusion is
  dependent upon the quality of the design and the
  care in execution.
• e.g. This experiment indicates that temperature
  affects the rate of enzyme reaction.
• e.g. No conclusion can be drawn from this
  experiment due to the large number of
  uncontrolled factors.