Practical Work In Biology

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Published on September 18, 2008

Author: GerryC

Source: slideshare.net

Practical Work in Biology Experimental Design Skills Practical Skills Presentation Interpretation and Evaluation

Experimental Design Skills

Practical Skills

Presentation

Interpretation and Evaluation

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 .

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 .

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

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

Dependent Variable The variable which may change as a result of changes to the in dependent 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.

The variable which may change as a result of changes to the in dependent 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.

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

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

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)

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)

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 0 0 C to 25 0 C the rate of enzyme action increased ; from 25 0 C to 30 0 C the rate remained the same .

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 0 0 C to 25 0 C the rate of enzyme action increased ; from 25 0 C to 30 0 C the rate remained the same .

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

Axes labeled with units

An appropriate scale (uniform and using most of the axis)

Accurate plot of points

Line of best fit

Tables Note the headings and units for the table Which piece of data has an inconsistent number of significant figures? Temperature ( o C) Rate of Enzyme Action (mLs -1 ) 0 0.0 5 12 10 24 15 34.2 20 42 25 49 30 50

Note the headings and units for the table

Which piece of data has an inconsistent number of significant figures?

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

The class data was plotted

Note choice of axes, scales, and units.

Describe which is the most appropriate line.

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.

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.

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.

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.

Systematic Errors Repeating an experiment may identify systematic errors. In repeating, a n 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.

Repeating an experiment may identify systematic errors.

In repeating, a n 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.

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 .

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 .

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.

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.

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 .

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 .

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.

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.

Relationship Between Precision & Accuracy systematic High precision low accuracy Low precision high accuracy (fluke) High precision high accuracy Low precision low accuracy errors still present random random & systematic

systematic

Relationship between precision and accuracy II P not A P and A A not P Not A not P

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 .

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 .

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 !

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 !

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

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

Resolution and Precision The resolution of the stopwatch is 0.01 s but the precision of the data does not match this. distance (cm) time (s) mean time (s) range (s) 40 0.90 0.98 0.93 0.95 0.94 0.08 80 1.25 1.29 1.27 1.21 1.26 0.08 119.5 1.54 1.54 1.44 1.41 1.48 0.13

Resolution and Precision The resolution of the stopwatch is now 0.1 s. distance (cm) time (s) mean time (s) range (s) 40 0.9 1.0 0.9 1.0 1.0 0.1 80 1.3 1.3 1.3 1.2 1.3 0.1 119.5 1.5 1.5 1.4 1.4 1.5 0.1

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.

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.

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.

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.

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.

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.

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