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Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
Practical skills in biology
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Practical skills in biology

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  • 1. Practical Skills in Biology Experimental Design Skills Practical Skills Presentation Interpretation and Evaluation Communication
  • 2. Experimental skills are examinable in the final examination Consult the syllabus (handout given out)
  • 3. HYPOTHESIS The starting point of any experiment – want to find out something. An idea which experiments are designed to test A testable statement (cause and effect) A statement that connects the independent and dependent variable e.g. 1. Light intensity will affect the growth of plants 2. An increase in temperature will affect the rate of enzyme action
  • 4. Independent Variable Also referred to as the experimental variable The variable which is deliberately changed Should be plotted on x-axis of a graph e.g. light intensity, temperature
  • 5. Dependent Variable The variable which may change as a result of changes to the independent variable Plotted on y-axis of the graph e.g. growth rate, rate of enzyme action
  • 6. Fair Testing Factors to be held constant All factors that are kept the same during an experiment An experiment can only have one independent variable. All other variables must be kept the same, this ensures a fair test Enables fair comparison e.g. light, temperature, amount, source, pH, concentration of enzymes etc
  • 7. Control A control is an additional experimental trial or run. It is a separate experiment, done exactly like the others. The only difference is that no experimental variables are changed. A control is a neutral "reference point" for comparison that allows you to see what changing a variable does by comparing it to not changing anything.
  • 8. Resolution Resolution is the smallest increment measurable by an instrument Resolution is a property of the measuring instrument It is determined by the number of digits from the measuring instrument (this should match the number of significant figures you use in your data) Resolution relates to individual measurements e.g. high resolution = 0.001g (electronic balance) Low resolution = 1.0g (kitchen scale)
  • 9. Presentation of Results All observations and measurements need to be recorded Construct tables with headings and appropriate units Draw graphs with a title which clearly connects the independent and dependent variables e.g. “The effect of varying enzyme concentration on the rate of respiration” Describe the results, do not explain them. e.g as the enzyme concentration increased from 20 mM to 50 mM the rate of respiration increased. Concentrations above 55 mM resulted in a decreased rate of respiration”.
  • 10. Enzyme Concentration (mM) Rate of Respiration (mLs-1) 5 10 0 12 15 20 45 24 32.9 42 50 55 45 0 Tables Note headings and units for the table Which piece of data is inconsistent? Why?
  • 11. Drawing Graphs Axes labelled with units and appropriate title An appropriate scale (uniform/use most of the axis Accurate plot of points Line of best fit
  • 12. Graph Rate of Respiration (mLs-1) The effect of varying enzyme concentration on the rate of respiration 60 50 40 30 20 10 0 -10 Series1 Linear (Series1) Log. (Series1) 10 20 30 40 5 10 15 20 45 50 55 Enzyme Concentration (mM) Average results were Plotted Note choice of axes, Scales, and units Which is the most appropriate line?
  • 13. Random Errors Random errors are caused by any factor that randomly affects the measurement variable The amount of random error is indicated by the amount of scatter in the data An increase in sample size allows averages to be calculated- this reduces the effect of random errors Measurements are never perfect- therefore random errors are always present e.g. inconsistent reading of scales/use of timer
  • 14. Systematic Errors Systematic errors are present when measured values consistently differ from their true value Usually due to faulty apparatus/equipment or experimental design Tend to be consistent throughout practical so an average does not rectify the problem. However repeating experiment may identify a systematic error (need to use other equipment) Consistent results indicate the conclusion(s) drawn are likely to be valid Balance not calibrated, contaminant in a solution
  • 15. Sample Size The number of samples in the experimental group Increasing the number of samples allows averages to be calculated Reduce the effect of random errors Data will be more consistent and reliable i.e. for each concentration of enzyme you may do replicates of 3.
  • 16. Reliability Refers to the extent which an experiment yields the same result on repeated trails under the same conditions Achieve reliability by minimising random errors Use large number of samples Be careful with measurements during the practical
  • 17. To repeat or not to repeat? Repeating the experiment with same procedure and apparatus at different times helps to identify systematic errors Repeat experiment to validate the results, experimental design and be confident in our conclusions Useful to repeat with different equipment, solutions etc… Are the results still the same?
  • 18. Validity Refers to the degree to which an assessment method measures what it is supposed to measure. It is increased by: 1. appropriate experimental design (testing what it is meant to test) 2. repeating the experiment
  • 19. Precision and Accuracy High precision, low accuracy High precision, high accuracy Low precision, high accuracy (fluke) Low precision, low accuracy
  • 20. Precision Precision depends on how well random errors are minimised Random errors are present when there is scatter in the measured values High scatter = low precision Low scatter = high precision
  • 21. 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 Detected by repeating experiment
  • 22. Precise or Accurate? Student A 4.3 5.0 4.9 4.4 4.7 Mean 4.6 Student B 4.5 4.6 4.6 4.5 4.5 4.5
  • 23. Resolution and Precision Distance (cm) time (s) mean (s) range(s) 40 0.9 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 The resolution of the stopwatch is 0.01s but the precision of the data does not match this.
  • 24. Resolution and Precision Distance (cm) time (s) mean (s) range(s) 40 0.9 1 0.9 1 1 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 The resolution of the stopwatch is now 0.1s
  • 25. Interpretation of Data (Discussion) Written in the third person (stated objectively) Inferences can be made when interpreting the data An inference is a 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 enzyme concentration influenced the rate of respiration as more enzyme was available for the reaction.”
  • 26. Analysis and Evaluation of the Experiment Identify sources and distinguish between random and systematic errors List ways to improve procedures of the experiment (possibly give reasons why) Comment on suitability and importance of the sample size Comment on the accuracy and precision of the experiment Comment on the value of repeating the experiment
  • 27. Conclusion A brief statement that relates to the hypothesis Should be written at the end of each experiment Supports or refutes the hypothesis Experiments do not prove the hypothesis Confidence in the conclusions will depend on the validity (design) of your experiment and the care in execution. e.g. “this experiment indicates that enzyme concentration does have an affect on the rate of respiration” or “no conclusion can be drawn from tis experiment due to the large number of uncontrolled factors”
  • 28. Other things to consider.. In the Materials and Methods, list the materials/equipment you actually used, and the method you used. It needs enough detail so that someone else could repeat exactly what you did. (Especially in a Design Practical) Write in Past Tense (Impersonal) Drawings may be used in the Results section Introduction- a brief review of the theory, state the aim and hypothesis of experiment.
  • 29. HAPPY EXPERIMENTING AND WRITING

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