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EXPERIMENTAL SKILLS AND SCIENTIFIC METHOD 2019 A Introduction: SCIENTIFIC METHOD Believe it or not performing scientific research is a fairly basic human activity which nearly every one performs as part of their daily life. Consider picking up a carton of milk. You observe that it is very light and you immediately make a logical guess that it is empty or almost empty. You then test this by looking inside the carton or swirling it around to see if this is true. Scientific method involves the same basic four steps. 1. Making Observations 2. Forming an Explanation called a Hypothesis 3. Testing your Hypothesis with an experiment. 4. Your hypothesis is Confirmed 5. Design more experiments to further support your hypothesis Your hypothesis is Refuted Write a new hypothesis HYPOTHESIS WRITING: A hypothesis is a statement that predicts the outcome of your experiment by stating the effect of the independent variable on the dependent variable. In Year 11 and 12 Biology a hypothesis is usually in the following form "If_______then________" . “If the independent variable is altered in such and such a way then the dependent variable will affected in such and such a way”. Eg. If the temperature is increased, the fish velocity will increase. The temperature is the independent variable and the fish velocity is the dependent variable. 1 This is an unacceptable hypothesis. “If the temperature is increased, the fish velocity will be affected.” “affected” is not specific enough. You need to predict the way that the dependent variable will be affected. A hypothesis must be actually testable. eg “the frog mated with the other frog because he thought she was attractive” is not actually testable because how can we know what a frog thinks. Hypotheses (p) can not be proved by an experiment. The experiment either supports the hypothesis (provides evidence for) or refutes the hypothesis (provides evidence against). An experiment can disprove a hypothesis. If a hypothesis is disproved by an experiment, a new hypothesis is made, taking into account all new evidence. Question Write a hypothesis for an experiment that is aiming to see the relationship between light intensity and photosynthesis rate. Hypothesis: Dep. Variable = Indep. Variable= Theory Variables are the factors which will affect the results of an experiment. The Independent Variable is the one variable that is being varied in different samples to see how it affects the results. The Dependent Variable is the factor that is affected by (or depends) on the independent variable and is measured as the results. Ie the results depend on what you vary in the experiment. Ie results = dependent variable Constants or Controlled Variables Are the variables kept constant in every sample to ensure they do not affect the results. All variables must be controlled to ensure that changes in the results can only be due to the changes in the independent variable and not other variables. Nb. The measuring instrument such as using the same ruler is not a controlled variable because it isn’t a variable in the first place (ie. It shouldn’t matter which ruler 2 you use, it won’t affect the results). Only if the choice of measuring instrument is likely to affect the results should that be included. A Fair Experiment must have 1 dependent variable 1 independent variable all other variables controlled. There must be only one independent variable so that you know that the results must be as a result of that one variable. If there were two independent variables you would not know which one is causing the results. If your experiment is not fair, it is not a valid experiment and the results mean nothing. Control A control sample or group is used to compare all other groups to. The control sample often does not have an independent variable or would represent what happens in the normal situation. Sample Groups - An experiment usually has a number of sample groups where each sample group tests a different value of the independent variable. Each sample group may have a number of samples that are treated in the same way. Sample size - big sample size for accuracy The sample size is the number of samples in a sample group. The results from all the samples in the sample group can be averaged together to get a more accurate average and increase the reliability of the results by averaging out the effects of random errors. Also having a large sample size allows for the identification and elimination of anomalous results (outliers). Example ; An experiment is testing the effect of colour on the absorption of heat by a painted glass beaker left in the sun for 10 minutes. There were 4 colours tested; 6 red beakers, 6 blue beakers, 6 yellow beakers, 6 white beakers and 6 unpainted beakers. What is the sample size? How many sample groups were there? What was the control group? What is an appropriate hypothesis for this experiment? List two advantages of using 6 beakers of each colour. 3 MEASURING RESULTS 1.Accuracy Accuracy is a measure of how true the measurements are to the real values. Accuracy is only looking at the measurement of the results rather than mistakes made in the design of the experiment. eg. a 2.20m pole may be measured at 2.0875m. This measurement has a high resolution ( many decimal places) but it is not accurate. Resolution and accuracy are entirely different. 2.21m is more accurate than 2.0875m. Errors reduce accuracy - Errors include random errors and systematic errors- 2. Random Errors – create scatter 3. Systematic Random errors result in the measurement sometimes being too low and sometimes too high in a random way. However, the average of a no. of measurements are fairly accurate. Systematic errors make the readings all too high by the same amount or all too low. It is usually the fault of the measuring equipment and the results can be adjusted after wards. Systematic errors are overcome by recalibrating the equipment with known standards and adjusting the measurements by the required amount. Eg. check a thermometer’s calibration in boiling water to see if it measures 100C. If it reads 99C all readings will be too low by 1C. The greater the sample size the more accurate the average. And therefore the more reliable the result. Please note; genetic variations in living members of a sample group cause scatter which are not strictly speaking random errors (it is more due to uncontrolled genetic variables). However, this scatter can also be averaged out using a large sample size. Errors – gives a consistent shift in results To check for systematic errors repeat the experiment. It is always useful to repeat an experiment with eg.1 Errors of Parallax exactly the same method but using different measuring apparatus. If the second set of When reading a thermometer, ruler, results are out by a set amount, then a measuring cylinder etc. your eyes must be at systematic error exists in either or both same level. If not the reading may be too high measuring devices used in the two or too low depending on your position. Even experiments. They will need to be calibrated if care is taken there is still some error of and the results will need to be adjusted. parallax. Errors of Calibration eg.2 Reaction times eg. When a thermometer has the numbers These effect your results often in a random printed on the side sometimes they are too high way. ie sometimes you are slower than other or too low. Therefore all measurements taken times. with that thermometer will be out by the same amount. eg. 3 air currents affect the mass reading on eg. If a student incorrectly zeros a balance then an electronic balance in a random way all the reading will be out by the same amount. eg. tape-measures can get stretched so all measurements are too low even though they may get proportionately lower as the measurement gets longer. Ie a 10cm length 4 may be out by 1mm but a 50 cm length will be out by 5mm. 3. Resolution A measuring cylinder. The resolution is the smallest difference possible between two measurements. Ie a measurement taken with a 100ml measuring cylinder is taken to the closest 1ml. Possible measurements are 1ml, 2ml, 3ml, 4ml etc. Therefore the resolution of these measurements is 1ml. The resolution of your measurements should not exceed the resolution of the measuring device, ie. the smallest increment that the measuring device can measure. Eg. a school ruler has a resolution of 1mm because this is the distance between the smallest lines. Therefore it is not possible to make a measurement of 33.5mm on a school ruler. It should be rounded up to 34mm. Resolution = Measurement = An ammeter. 4. Precision The precision of results is a measure of how scattered the results are due to random errors. The more scattered, the less precise Resolution = Measurement = High Precision 4.32 4.33 4.35 4.33 Low Precision =scatter 4.25 2.88 4.02 5.33 5. How many decimal places to take your measurements to. Resolution Your measurements cannot be taken to a greater resolution than the resolution of your measuring equipment. Reproduce-ability Eg. the height of a person should be measured to the closest cm eg. 156 cm = 1.56m And not to the closest mm eg. 1568mm =1.568m because you will always get the same measurement in cm but not in mm. 5 Your measurements should offer the greatest Changes in posture and error of parallax will resolution possible but not so much that affect the measurement in mm so much that it errors cause significant scatter. If there is too will not be reproducible if measured again. much scatter from errors, the measurement will not be able to be accurately reproduced if measured a second time. There is no point in measuring to a resolution which doesn’t give accuracy. 6. Significant Figures in Calculations If you are multiplying or dividing measurements you took in an experiment, your answer must have the smallest number of significant numbers of any of the measurements used in the calculation. 7. Eg. 25.6 /2.0 = 12.8 rounded off to 13 (2SF because 2.0 has 2 SF Eg2. 14.34 x 212 = 2868 rounded off to _________ Calculating averages – the no. of decimal places of any average (mean) is the same as the minimum number of decimal places of any of the values used to make the average. Eg. 7.5, 5.0, 3.3 3.45 are averaged to be 4.8125 cancelled down to 4.8 Calculate the average of 17.2 and 13.3 ………………. calculate the average of 0.2 &0.6 & 0.5 &0.4 …………….. Reliability Reliability is the consistency of your measurement, or the degree to which an instrument measures the same way each time it is used under the same condition with the same subjects. In short, it is the reproducibility of your measurement. sample size 3 with random errors Sample size 10 with random errors Expt 1 Expt1 17,25,11 ave 17.7 Expt 2 14,22,29 17, 25,11,23,29,13,15,19,15,22 ave 17.4 Expt 2 ave 21.7 With a sample size of three, the two averages are not similar; Therefore the scatter due to random errors are still affecting the average and the results (averages) are not reproduceable ie they are not reliable. 14,22,29,12,14,16,20,19,14,13 ave 17.3 With a sample size of ten, the two averages are almost the same. This is because a sample size of ten is large enough to average out the effects of the scatter due to random errors. There fore the results (averages) are reproduceable ie they are reliable. 6 The larger the sample size, the more reliable the average because a large sample averages out the effects (ie. scatter) caused by random errors. Please learn this off by heart. Repeating Experiments Repetition is when a whole experiment is repeated following the exact method at a different time but using different equipment. The aim is to see if the results are the same and therefore reproducible. If the results are reproducible it indicates that the results are reliable. (see below) If the results of a repeated experiment are the same, this indicates two things 1. your results are reproducible meaning that there are no systematic errors causing a shift in results. 2. you have controlled the variables well ie it is a fair experiment. 3. Your sample size is large enough to remove the scatter caused by random errors. These three things help to demonstrate that the results are RELIABLE. It also partly indicates that the design of the experiment is valid as it indicates that there are no uncontrolled variables influencing the results. If the repeated results show the same trend but there is a consistent shift of the trend there is probably a systematic error. If the repeated results show a completely different trend, the original experiment is made invalid and disregarded. This may have been because 1. the original results were a “Fluke” caused by a freak of chance with extreme random errors. 2. the results could have been fraudulent to support the ego of the experimenter. 3. There is an uncontrolled variable affecting the results that you are not aware of. The role of repeating the experiment is not to get more accurate results. You can not average the results from two different experiments together because the conditions would have been slightly different and the equipment was probably different. The role of repeating the experiment with different equipment is to 1. check for systematic errors ie if there is a systematic error , the same trend and pattern will be detected , but the actual measurements will be shifted by a consistent amount. This could mean that there was a calibration error in the equipment. It could also mean that the slightly different conditions or biological sample was causing the difference. 2. verify the results to see if they have a level of accuracy, truth and reproducibility (verify means to find out if something is true by authenticating it from other sources) 7 TABLES: You need to be able to design a table with 1. an explanatory title -" the Effect of the Indep. V. on the Dep.V" 2. labelled column headings (Indep.V in the left column and Dep. V in the right column) 3. units in the column headings 4. all data with the same no. of decimal places. 5. Averages rounded off to the same no. of decimal places as the data. 6. any anomalous results can be identified with an asterisk or highlighter and made note of. Anomolous results are not included in averages. Table Title : The Effect of Age on Height of Boys Units in headings(even %) Indep.V All Ave AGE (years) 10 11 12 13 Dep V. Ave. Dep V Height of boys (m) 1.11 1.25 1.09 1.56 1.21 1.22 1.35 1.60 1.05 1.45 1.46 1.40 1.01 1.31 1.39 1.35 Average Height(m) 1.10 1.31 1.32 1.48 Data has the same no. of dec. pl has same dec.pl as data Please don’t label things like sample 1 sample 2 etc. Quiz 1. What is wrong with the Table below? A B C Highest Speeds 17km 13 11.56 19km/hr 14 10.93 Average 18 13.5 11.245 8 2. Draw a table below to show the following information. A student looked at the affect of temperature on the number of eggs produced by a number of fish. At an average temperature of 15ºC five fish produce 1010, 1300, 1044, 1066, 1219 eggs. At 4ºC the fish produced 1987, 2061, 1832,1435, and 5 eggs. At 20ºC the fish produced 25, 103, 84, 34 and 59 eggs. GRAPHS You need to be able to draw a graph with 1. an explanatory title, “The Effect of the Indep.V on the Dep.V” 2. x axis (or horizontal axis) with the independent variable, 3. y axis (or the vertical axis) with the dependent variable. D I 4. axes labelled 5. units stated in axis labels 6. correct form of graph eg a column graph- discontinuous x axis eg apples and pairs eg. A line graph - continuous x and y axis eg. time, mass, volume etc. 7. the scales increase evenly on the axes (a break in scale can only be used if it is not within the plotted region of the graph- otherwise it affects the shape of the graph) 8. use a line of best fit or a curve of best fit if random errors, genetic variation etc. in the specimens have caused scatter in the results. The line of best fit averages out the scatter so that a trend can be more easily seen. When drawing a line/curve of best fit there should be almost even numbers of points on either side of the line/curve and the line/curve should be as close to the points as possible while maintaining a smooth shape. 9 Extension : a good line of best fit will have the sum of the perpendicular distances of the points from the line on one side equal to the sum on the other side. 9. Interpolate (inside graph) and exptrapolate (outside graph) points ie use a graph to predict the value of unknown values using the line/curve of best fit. 10. The graph should be large and must not be less than half the size of the graph paper provided. Describing trends in graphs. You need to say whether the graph is increasing or decreasing and then also whether it is changing steadily (straightline) or exponentially (at an increasing rate). 10 Drawing lines of Best fit 11 Activity The effect of the blood alcohol concentration on reaction time. Blood alcohol concentration (g/100ml) Reaction Time (sec) 0.04 0.256 0.06 0.265 0.08 0.312 0.09 0.364 0.10 0.422 Plot the data on a graph Make the alcohol conc.axis extend to 0.11g/100mL 2. Predict the reaction time at O.07 and 0.11g/100ml of alcohol. 3. Describe the trend of the graph. 4. Comment on the number of random errors observed. 12 MAKING CONCLUSIONS Conclusions 1. need to be statements that are definitely true according to the results. 2. need to be as specific as possible. 3. are not generalisations or assumptions. 4. must not include any inferences. Inferences are attempts to explain why the results turned out as they did. Possible conclusions include 1. The radish plants have an optimum growth rate in height between the temperatures of 25C and 30C. 2. Radish plants do not grow in height at temperatures lower than 5C or higher than 55C. You can not say "Radish plants grow faster at 30C because their enzymes are working faster". You didn't test their enzymes so you can't conclude that was the reason why. This is an inference. You can not say that "plants grow fastest between the temperatures of 25C and 30C". You only tested radishes , not other plants and it probably wouldn't be true for other species. Additionally , you only tested height not growth in the no. of leaves and other growth aspects. Be careful not to over generalise. 13 Question. The graph below which shows the concentrations of 3 different sugars in whisky mash as it ferments. List 2 appropriate conclusions for this graph. 3. List two conclusions about the hormone CRH. 14 EVALUATING AN EXPERIMENT Evaluating anything is determining a “value” for it. Evaluating an experiment means that you have to use arguments to decide whether the experiment is of value – ie how good or bad it is? This is done be weighing up its relative strengths and weaknesses. In our work this year you will need to be able to access whether an experiment was designed well or if the data was reliable. Strengths in an experiment that can be included are state the conditions that were well controlled eg. the temp. was kept constant by ……… the measurements could be taken reasonably accurately. Eg. the distance was taken with a mm ruler lying right next to the object to avoid the error of parallax. the measurements had a high resolution. there was a lot of replication (a large sample size of more than 5) so that the effects of random errors and individual variation could be averaged out. This would make your results more RELIABLE. The results showed a consistency suggesting that there were not many uncontrolled factors affecting the results. There may have been many sample groups so that many points on the graph could be drawn. Weaknesses in an experiment that can be considered. Please only discuss weaknesses that have a significant impact on the results. Consider which variables were not controlled adequately even if an attempt was made to control it. Could they be affecting your results significantly? Consider whether the design is fair or whether it is not really testing the hypothesis. Consider the sources of error when taking measurements and how much of an impact these errors are making on the results. Consider whether the sample size was too small (0 to 5) to make a reliable average. Consider whether there were enough points on the graph to see a clear trend. If not which additional sample groups should be tested to see a better trend. Any other design faults which are peculiar to the experiment Was your experiment valid? Ie Are your experimental results and therefore your conclusions true? 15 Writing a Practical Report in the Correct Format You will need to write your practical reports in the correct format. The method we will use to write practical reports is a copy of the method that scientists write up their research in journals for other researchers to read. Scientific reports are written in past, impersonal tense. These scientific reports always have the following sections in this order. 1. Abstract: This is a short, paragraph summary of the entire practical including the aim, method, results and conclusions. The abstract is designed for scientists to read the experimental findings in brief to see if they want to read the whole report in detail. 2. Introduction This section is designed to give any reader the background information which lead the scientist into deciding to do this experiment. You will need to write the scientific background of what this experiment is about. It is usually about 100 to 200 words long. This should be in-text referenced where relevant. 3. Aim A sentence to explain the purpose of the experiment. 4. Hypothesis A prediction for the results of the experiment that relates the dependent variable and independent variable. 5. Variables A list of the independent, dependent and at least 4 controlled variables. 6. Apparatus This is list of special equipment and materials needed for the experiment. You must include appropriate particulars about the materials which may effect the results.eg. size of the equipment and the exact concentrations of solutions etc. 7. Method You must give a list of what was performed in the practical in enough detail for another student to follow your method and be able to perform an identical experiment and get the same results! This is best written as a series of numbered steps. A diagram drawn in pencil in the correct scientific format may need to be added to add clarity. Do not forget to make this in past tense. Be careful to include details like the number of samples tested and the exact way the results were measured. Remember if you repeat something twice, it is done 3 times. Ensure that at the end of the method that you assess the safety implications of this experiment. Ie what precautions did you need to make during the experiment and possible hazards. 8. Results This section should display your observations and measurements in a form that the reader can easily make their own conclusions. The best way is to devise simple tables and graphs which are labelled and titled. Other significant notes and qualitative observations can be written down. You may need to manipulate the data such as finding averages or reciprocals or calculate rates. Anything to make the trends in the data clear so the aim of the experiment can be achieved. Ensure that you have the correct no. of Sig. Fig. and the averages are calculated with the correct no. of decimal places. 9. Discussion This section needs to analyse the results and say what the results mean. It also evaluates the experiment to see whether the results were actually valid. 16 ANALYSIS Analyse your data. Go through each table and its accompanying graph one by one. For each graph/table 1. Describe the trend shown by the graph. 2. Describe how reliable this data is according to a. how much scatter is within each sample group, and b. how well the average fit the line of best fit. This will tell you how much scatter is left in the average. Decide whether the sample size was large enough to average out the scatter effect of random errors. 3. Make your conclusions about what this experiment shows you. Be precise and exact- no inferences and over generalisations. 4. Explain why you think this trend exists in terms of the science and biology you know. Use terms such as “this is probably because” or “ maybe this is because” because you are guessing why. The explanation may need to be lengthy and it should cover all parts of the trend you see. Diagrams can be included here. 5. State whether the data confirms or refutes the hypothesis. EVALUATION The second part of the discussion is your evaluation of the experimental design and procedure and how you would improve the design to overcome these design flaws. 1. Critically and logically evaluate the experimental design by briefly considering the strengths and fully analysing the weaknesses. Focus on the weaknesses. The weaknesses could be the following. a. Look at each measurement you took through out the method and decide if there were there problems in the way the measurement was taken that lead to a lot of errors? Could these measurement methods be improved? b. Was the sample size large enough?- how do you know? If not how big should it be. c. Were there enough sample points on the graph? If not which ones would you include next time.? d. Were there some controlled variables that were not controlled adequately? If so how would you control them adequately next time? e. Was the test fair? f. Was the test actually designed to test what we were looking for? g. Were there any systematic errors that would effect the conclusion of your results? Most will not make much difference. Do not discuss every little problem that would not significantly affect the results. In light of the above discussion of the method, determine whether you still consider the experiment and results valid. 2. Suggest additional improvements to the ones you have already discussed especially ones that would improve the major design flaws that you identified. These improvements should be clear, practical, new ways of doing the experiment using equipment that already exists. 10. Conclusion: A list of conclusions you can make. You may be repeating the conclusions you have talked about in the discussion. Usually there is more than one conclusion that you can make from the data. They should be highly refined and specific. Ensure that these statements are about what the data is indicating not your possible explanations of the data ie inferences. Do not comment on the success, enjoyment or failure of the experiment. 17 : 11. Further Enquiry: comment on the significance of your findings and any further experiments that could be conducted to find out something new which comes out of this investigation. Experiments may be used to test hypotheses. State a testable hypothesis, where appropriate. Designing Investigations and Experiments Design Scientific inquiry involves designing procedures, including practical investigations based on the scientific method or observations made in the field, to investigate questions. Designing an investigation involves identifying: what needs to be observed the measurements that need to be taken the techniques that need to be used the apparatus or measuring instruments needed. Design and carry out investigations to explore posed questions or hypotheses using the scientific method. Every step in a practical or issues investigation serves a purpose. Describe the steps of an investigation. Design and carry out experiments to investigate a biological issue. Record and analyse observations. Draw or interpret diagrams of the apparatus used in an experiment. Variables Many practical investigations involve deliberately changing one quantity and determining the effect on another quantity. These quantities are referred to as ‘variables’. Identify the variables in a practical investigation. The quantity being deliberately changed is called the ‘independent variable’. The quantity that changes as a result is called the ‘dependent variable’. Classify the variables in a practical investigation as independent or dependent. Other factors are held constant, if possible, throughout a practical investigation. Identify any factors that are deliberately held constant throughout a practical investigation. Conducting Investigations Procedures Practical investigations require a particular set of actions to be carried out in a well-defined order. Follow instructions accurately and safely. Safety and Ethics Ethical practices must be followed when conducting practical and issues investigations. Work ethically with animals. Safety must be considered when conducting investigations. Recognise hazards and work safely during an investigation. Many investigations involve the collaborative efforts of a team. Negotiate procedures with the other members of the team. Define the role of each member. Members of a team work together. Perform the role of a team member. Maintain confidentiality, report accurately, and acknowledge the work of other people. Errors in Measurements Measurements are affected by random and/or systematic errors. Identify sources of errors and uncertainty that may occur in an investigation. 18 Random errors are present when there is scatter in the measured values. Systematic errors are present when measured values differ consistently from the true value. Distinguish between random and systematic errors. Where applicable, increasing the number of samples minimises the effects of random errors and improves the reliability of the data. Explain the importance of increasing the number of samples in a practical investigation. Systematic errors can be identified and results verified by repeating an experiment using an alternative source of equipment and materials. Explain the importance of repeating a practical investigation where possible. Precision, Reliability, and Accuracy The reliability/precision of data collection is related to the reproducibility of the measurements. Where possible, collect data using measurements that can be reproduced consistently. Measurements are more reliable when there is less scatter in the results. Determine which of two or more sets of measurements is most reliable. Reliability depends on the extent to which random errors are minimised. Use averages or graphing as a means of detecting or minimising random errors. The accuracy of an experimental value indicates how close the result is to the true value and depends on the extent to which systematic errors are minimised. State which result of two or more experiments is most accurate, given the true value. The resolution of a measuring instrument is the smallest increment measurable by the measuring instrument. Select an instrument of appropriate resolution for a measurement. The number of significant figures for a measurement is determined by the reproducibility of the measurement and the resolution of the measuring instrument. Record and use measurements to an appropriate number of significant figures. Information and Data Investigations involve evidence, which may be quantitative or qualitative. Distinguish between quantitative and qualitative evidence. Valid conclusions depend on gathering appropriate evidence. In investigations, make and record careful and honest observations and measurements. Data can be more easily interpreted if presented in a well-structured table. Present data in an appropriate tabular form. Include a title, column headings showing the quantities measured and units used, and the values observed or researched. Graphs are a useful way of displaying data. When a graph is plotted, the independent variable (or a quantity derived from it) is plotted horizontally and the dependent variable (or a quantity derived from it) is plotted vertically. Plot a graph of dependent variable versus independent variable. Include a title, labelled axes, and appropriate scales and units. A line of best fit can show relationships between variables in an experiment. Draw a line of best fit through a series of points on a graph such that the plotted points are scattered evenly above and below the line of best fit. Understanding of a topic, issue, or question is enhanced using information from different sources. Obtain information from different sources. Information obtained must be critically examined for accuracy and suitability for the purpose for which it was sought. Evaluate for bias, credibility, accuracy, and suitability the information obtained from a source. The source of information must be recorded so that the information is accessible to others. List the sources of information using an appropriate format. Interpretation and Evaluation Careful observation in a practical investigation is essential for analysis and for comparison with other investigations. Describe a pattern observed in the results of an investigation. 19 The scatter of data points above and below the line of best fit is probably due to random errors. Using the scatter in the graphs of data from similar investigations, compare the random errors. Subsequent investigations can be improved by the critical evaluation of the procedure and results. Analyse and evaluate information from a series of observations or an investigation, and suggest improvements or indicate the additional information needed. A conclusion should be written at the end of each investigation. Write a conclusion that is based on the results of an investigation and related to the question posed and the purpose of, or the hypothesis for, the investigation. 20