1. Internal Assessment Your overall IB mark (the one sent to universities after the IB exam) 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 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

6. 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.” 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. Be sure your hypothesis is related directly to your research question and that the manipulated and responding variables for your experiment are clear.

7. Design—D (Aspect 2)

8. 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. ).

9. Design—D (Aspect 3)

10. 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. 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.

11. 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. 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?

12. 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.

13. Data collection and processing—DCP

15. 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.

16. 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.

17. 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.

18. 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.

19. Data Collection and Processing Aspect 2: Processing Raw Data

20. 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. The recording and processing of data may be shown in one table provided they are clearly distinguishable.

21. 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.

22. Descriptive Statistics Statistics are useful mathematical tools which are used to analyze data. For more information about statistics, click here. click here Statistical Analysis Mean Standard Deviation T-Test T-Test in Excel or on TI-83 Chi squared test

23. Data Collection and Processing Aspect 3: Processing Raw Data

24. 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.

25. Conclusion and Evaluation

27. 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.

28. Conclusion and Evaluation Aspect 2: Evaluating Procedure(s)

29. 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. 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.

30. 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.

31. Conclusion and Evaluation Aspect 3: Improving the Investigation

32. 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