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  • 1. Internal Assessment Your overall IB mark (the one sent to universities after the IB test) in any IB science course is based upon two kinds of assessments or grades:  External Assessment:   Your score on end-of-course exam (76% of total IB mark) Internal Assessment:   Your performance on in class laboratory work (24% of total IB mark)
  • 2. Internal Assessment Criteria
    • IB lab reports are graded using three parts
      • They are:
      • Design—D
      • Data collection and processing—DCP (includes lab drawing, statistics and graphing)
      • Conclusion and evaluation—CE
  • 3. Design
  • 4. Design—D (Aspect 1)
  • 5. Design Aspect 1 :   Defining the Problem and Selecting Variables
    • Research Question
    • This is a single sentence which clearly and specifically states the objective of your investigation.  For a Design lab, the teacher cannot give you detailed information and guidance.  Instead, you’ll be given a general, open-ended problem such as “Investigate the factors that affect X”.  You must do some thinking to recognize the nature of the problem that has been set, the factors (variables) that will affect the outcome, and how they affect it (the hypothesis).  So if a general question has been posed, make it more specific and relevant to your individual experiment.
    • If you're doing a controlled experiment, your research question must clearly identify the manipulated and responding variables for your experiment.
  • 6. Design Aspect 1 :   Defining the Problem and Selecting Variables
    • Selecting Variables
    • State variables explicitly, and explain why each is relevant.  All reasonable variables that might affect the outcome should be identified.  Indicate which variable(s) is/are manipulated variables (ones that you will change) and which are the responding variables (ones that will respond to what you did).  Indicate which variables must be controlled and why those variables must be controlled.
    • The variables need to be explicitly identified by the student as the dependent (measured), independent (manipulated) and controlled variables (constants). Relevant variables are those that can reasonably be expected to affect the outcome
  • 7. Design Aspect 1 :   Defining the Problem and Selecting Variables
    • Hypothesis(es)
    • Although not required by the IB Organization, for many labs you will be asked to include a hypothesis.  A hypothesis is like a prediction.  It will often take the form of a proposed relationship between two or more variables that can be tested by experiment:  “If X is done, then Y will occur.”  (Examples:  “The rate of transpiration will increase as wind speeds and temperatures rise” or “Brand X toothpaste will be more effective in preventing the growth of the bacteria which causes plaque on your teeth”).
    • You must also provide an explanation for your hypothesis.  This should be a brief discussion (paragraph form) about the theory or ‘why’ behind your hypothesis and prediction.  For example, why should raising the temperature and increasing wind speed increase the rate of transpiration?  Why is brand X toothpaste more effective in preventing the growth of the bacteria which causes plaque on your teeth? 
    • Be sure your hypothesis is related directly to your research question and that the manipulated and responding variables for your experiment are clear.
  • 8. Design—D (Aspect 2)
  • 9. Design Aspect 2 : Controlling Variables
    • Control of Variables
    • “ Control of variables” refers to the manipulation of the independent variable and the attempt to maintain the controlled variables at a constant value. You should write a paragraph in which you describe how the control of variables is achieved. If the control of variables is not practically possible, some effort should be made to monitor the variable(s).  State an explicit procedure or method for how each variable will be controlled.  (For example, if the temperature must remain constant, figure out how you will do this and state it.  Perhaps you might use a water bath that is maintained at a certain temperature.  Or perhaps the amount of light must remain constant.  In this case, you might take light readings before and after the experiment).
  • 10. Experiment
    • Variable – factor in the experiment that is being tested
  • 11. Experiment
    • A good or “valid” experiment will only have ONE variable!
  • 12. Design—D (Aspect 3)
  • 13. Design Aspect 3 : Developing a Method for Collection of Data
    • Apparatus and Materials
    • Consider making a list of your experiment and materials needed.  Be as specific as possible.  (Example:  “50 mL beaker instead of ‘beaker’, type of microscope with magnification range).
    • A diagram or photograph of how you set up the experiment may be appropriate, especially for more complicated experiments.  Be sure your diagram includes a title and any necessary labels.
    • You might have to decide how much of a substance or a solution to use.  If so, state your reasoning or show the calculations .
  • 14.  
  • 15. Design Aspect 3 : Developing a Method for Collection of Data
    • Method/Procedure
    • State the procedure that you are going to use in the experiment.  This should be in the form of a list of step-by-step directions.  Provide enough detail so that another person could repeat your work by reading your report! 
    • If you do something in your procedure to minimize an anticipated error, mention this as well.  (Example:  “Carefully cutting plant stem under water to reduce affect of air on transpiration rate.”) 
    • In your method, clearly state how you will collect data .  What measuring device will you use, what data will you record, and when?  Or what qualitative observations will you look for (such as color change) and what will you do when you see this happen?
  • 16. Design Aspect 3 : Developing a Method for Collection of Data
    • Multiple Trials
    • The procedure must allow collection of sufficient relevant data.  The planned investigation should anticipate the collection of sufficient data so that the aim or research question can be suitably addressed and an evaluation of the reliability of the data can be made.
    • As a rule, the lower limit is five measurements, or a sample size of five.
    • The data range and amount of data in that range are also important. For example, when trying to determine the optimum pH of an enzyme, using a range of pH values between 6 and 8 would be insufficient. Using a range of values between 3 and 10 would be better, but would also be insufficient if only three different pH values were tested in that range
  • 17.  
  • 18. Data collection and processing—DCP
  • 19. Data collection and processing—DCP
  • 20. Data Collection and Processing Aspect 1: Recording Raw Data
    • You must collect and process data accurately.  But equally important—you must present the data so the reader can easily interpret it.  This means it must be organized and legible.  The best way to present data is by using data tables. 
  • 21. Types of Data
    • Raw data is the actual data measured. The term “quantitative data” refers to numerical measurements of the variables associated with the investigation.
    • Qualitative observations are just as important as quantitative measurements!  Make sure you take note of and record the physical characteristics of substances or solutions involved in the experiment, their changes, whether something is hot or cold, etc. 
  • 22. Units
    • A measurement without units is meaningless!  When you make quantitative observations you are expected to use the appropriate units. The system of units used is the International System of Units - SI units. In the table below you are given some of the more common SI units you will need to use .
  • 23. Uncertainties
    • All measurements have uncertainties and you must indicate them in your data tables.  This is best done by paying attention to significant digits, and by using the ‘plus-or-minus” (+/-) notation. 
    •   Examples : 
    • Mass of a penny on a centigram balance:  3.12g (+/- 0.05g) 
    • Temperature using a typical lab thermometer:  25.5°C (+/- 0.5 °C) 
    • For our purposes, the accuracy of a measurement device is one half of the smallest measurement possible with the device.  So, for example, the rulers in class measure to the millimeter (0.1 cm).  So, the ruler’s measurement uncertainty is +/- 0.05 cm.  Just as for units, in a column of data you can show the uncertainty in the column heading and then you don’t have to keep re-writing if for every measurement in the table.
  • 24. Data Collection and Processing Aspect 2: Processing Raw Data
    • This is the part of the report in which you take your raw data and transform it into results that answer (hopefully!) your research question.   
    • Data processing involves, for example, combining and manipulating raw data to determine the value of a physical quantity (such as adding, subtracting, squaring, dividing), and taking the average of several measurements and transforming data into a form suitable for graphical representation. It might be that the data is already in a form suitable for graphical presentation, for example, distance traveled by woodlice against temperature. If the raw data is represented in this way and a best-fit line graph is drawn, the raw data has been processed.  
    • The recording and processing of data may be shown in one table provided they are clearly distinguishable.
  • 25. Calculations of Results
    • You will often have to show calculations.  Use plenty of room; make sure they are clear and legible.  Show the units of measurements in all calculations.
    • Pay attention to significant digits!  Don’t lose accuracy by carelessly rounding off.  Round only at the end of a calculation.  Do not truncate.
    • Identical, repetitive calculations do not have to be repeated.  Show one sample calculation (labeling it as such) and then you don’t have to repeat it for all the trials, but only show the results obtained.
    • When calculating an average value from repeated trials, don’t average the raw data.  Instead, calculate a result from each trial.  Then average the results from each trial to get your final experimental average.
  • 26. Descriptive Statistics
    • Statistics are useful mathematical tools which are used to analyze data.   For more information about statistics, click here .
    • Statistical Analysis
    • Mean
    • Standard Deviation
    • Standard Deviation in Excel 2003 or on TI-83
    • Mean and Standard Deviation in Excel 2007 (doc)
    • Mean and Standard Deviation in TI- nspire (external link)
    • T-Test
    • T-Test in Excel or on TI-83
  • 27. Data Collection and Processing Aspect 3: Presenting Processed Data
    • Students are expected to decide upon a suitable presentation format themselves (for example, spreadsheet, table, graph, chart, flow diagram, and so on). There should be clear, unambiguous headings for calculations, tables or graphs. Graphs need to have appropriate scales, labeled axes with units, and accurately plotted data points with a suitable best-fit line or curve (not a scatter graph with data-point to data-point connecting lines). Students should present the data so that all the stages to the final result can be followed. Inclusion of metric/SI units is expected for final derived quantities, which should be expressed to the correct number of significant figures. The uncertainties associated with the raw data must be taken into account.
    • For more information about graphing, click here .
  • 28. Conclusion and Evaluation
  • 29. Conclusion and Evaluation
  • 30. Conclusion and Evaluation Aspect 1: Concluding
    • The conclusion starts with one (or more) paragraphs in which you draw conclusions from your results, and whether or not your conclusions support your hypothesis.  Your conclusion should be clearly related to the research question and the purpose of the experiment.  You must also provide a brief explanation as to how you came to this conclusion from your results.  In other words, sum up the evidence and explain observations, trends or patterns revealed by the data.
    • When measuring an already known and accepted value, you should draw a conclusion as to your confidence in the result by comparing the experimental value with the textbook or literature value. The literature consulted should be fully referenced .
  • 31. Conclusion and Evaluation Aspect 2: Evaluating Procedure(s)
  • 32. Conclusion and Evaluation Aspect 2: Evaluating Procedure(s)
    • The design and method of the investigation must be commented upon as well as the quality of the data.  You should consider how large the errors or uncertainties are in your results.  How confident are you in the results?  Are they fairly conclusive, or are other interpretations/results possible?
    • Identify and discuss significant errors and limitations that could have affected the outcome of your experiment.  See the pages on statistics and graphing for help in determining significance. Were there important variables that were not controlled?  Were there flaws in the procedure you chose which could affect the results?  Are measurements and observations reliable?  Was there a lack of replication?
    • Your emphasis in this section should be on systematic errors, not the random errors that always occur in reading instruments and taking measurements.  You must identify the source of error and if possible, tie it to how it likely affected your results. 
    • Acceptable Example: 
  • 33. Acceptable Example:  
    • “ Because the simple calorimeter we used was made from a tin can, some heat was lost to the surroundings—metals conduct heat well.  Therefore, the value we obtained for the heat gained by the water in the calorimeter was lower than it should have been.”
    • Unacceptable Examples : 
        • "The test tubes weren’t clean.”
        •  “ Human error.”
    • You must not only list the weaknesses but must also appreciate how significant the weaknesses are. Comments about the precision and accuracy of the measurements are relevant here. When evaluating the procedure used, the you should specifically look at the processes, use of equipment and management of time.
    • For more information about error analysis, click here .
  • 34. Conclusion and Evaluation Aspect 3: Improving the Investigation
  • 35. Conclusion and Evaluation Aspect 3: Improving the Investigation
    • Suggestions for improvements should be based on the weaknesses and limitations identified in aspect 2.
    • Modifications to the experimental techniques and the data range can be addressed here. The modifications proposed should be realistic and clearly specified.
    • Suggestions should focus on specific pieces of equipment or techniques you used.  It is not sufficient to state generally that more precise equipment should be used.  
    • Vague comments such as “We should have worked more carefully” are not acceptable