IB Biology Lab Assessment Standards

Research Design:

This criterion assesses the extent to which the student establishes the scientific context for the work, states a clear and focused research question and uses concepts and techniques appropriate for the Diploma Program level.  Where appropriate, this criterion also assesses awareness of safety, environmental, and ethical considerations.

A research question with context should contain reference to the dependent and independent variables or two correlated variables, include a concise description of the system in which the research question is embedded, and include background theory of direct relevance.

Research Design Further Explanation:

• Background: Just like a history or english paper, scientific research/lab reports have an introduction, referred to as a background.  The background should be a paragraph or two that explains why the experiment is relevant (to both biology and life), discusses what process or mechanism will be experimented, and includes background information necessary to understand the scope of the experiment.  The background should include cited sources (use a separate works cited page) for background information pertaining to the topic at hand.
• Research Question: A single sentence that specifically states the objective of the investigation.
• Variables: Variables must be specifically identified and explained why relevant.  Variables that will be manipulated and recorded on the graph’s x axis (independent), those that will respond to manipulations, be measured, and recorded on y axis (dependent), and those that are not manipulated (control) must be identified.
• Control: A control refers to standard or reference treatment or group in an experiment.  It is the same as the experimental (test) group, except that it lacks the one variable being manipulated by the experimenter.  Controls are used to demonstrate that he response in the test group is due to a specific variable (e.g. temperature).  The control undergoes the same preparation, experimental conditions, observations, measurement, and analysis as the test group; this helps to ensure that responses observed in the treatment groups ban be reliably interpreted. 
• Hypothesis(es): The hypothesis(es) should be composed to identify the analysis of the relationship between two or more variables; ‘If independent variable(s) is manipulated, then prediction to dependent variable.”  It should be assumed, but they hypothesis must be directly related to the question of research.
• Materials:

• Create a list of experimental materials/apparatus.  Be specific.
• If selecting a quantity to use during the course of the experiment, justify the quantity you select.

• Method & Procedure:

• A description of how the experiment will be set up; this should be supplemented by diagrams, sketches, or photos to illustrate the procedure.
• List (numbered list) the means that the experiment will be conducted using a detailed numbered list.  The procedure should include sufficient detail so that anyone reading your work could repeat your experiment.
• Routine actions, such as using a thermometer to check the temperature does not need to be explained but can be simply stated.
• If a standard technique is used, this can be used and referenced to as part of the procedure as long as a citation of the source is provided.
• Discuss all actions done to minimize measurement error
• Clearly state how data will be collected and what anticipated qualitative observations you anticipate.
• Indicate all forms of measurement and indicate the measurement uncertainty of each

• Controlling Variables:

• All conditions that could affect the outcome of the experiment must be controlled for or removed except for the independent variable.  This ensures that all results and data collected are directly due to the independent variable.
• This should be accomplished by a paragraph that explains how variables will be controlled and a description of procedure or method for controlling each variable.  For example, if the salinity of a solution is to remain constant throughout the duration of a test, the salinity values could be tested before and after the collection of data.

• Multiple Trials:

• The procedure must provide for sufficient collection of data to complete analysis of results.  Sufficient data is a rather vague term, but a safe conclusion is to perform multiple trials unless the experiment time frame is multiple months/year(s).  A good rule of thumb is five measurements for a lower limit and 20 measurements on the high end.  At minimum, you should use 5 different variants of your independent variable and 5 trials for each variant,
• If one of your trials is significantly different than all others, it may be excluded with a justification for your exclusion (for example if enzyme rates generally range from 1 to 5 kpa/min, the removal of a data point at 20 kpa/min would be appropriate).
• List and describe safety precautions that must be taken during the lab, i.e. wear safety goggles throughout duration of experiment, avoid breathing vapors, etc.

Analysis:

This criterion assesses the extent to which the student’s report provides evidence that the student has selected, recorded, processed and interpreted the data in ways that are relevant to the research question and can support a conclusion.

Analysis Further Explanation:

• Raw Data:

• Recording for raw data: quantitative data refers to numerical measurements, qualitative data refers to observations.  Both are equally important and should have corresponding data tables.
• Raw data should be organized in a table with labeled units and uncertainties and put in the appendix of a report.

• Tables & Measurements:

• Give a specific identifying title to each data table.  Number tables consecutively throughout the report.
• Units! If making quantitative measurements you must include SI units.  These can be found here.
• All measurements must have uncertainties and must be indicated in data tables.  This can be completed by using the (+/-) notation.
• The accuracy of measurements is one half of the smallest measurement possible.  For example, a ruler would have an uncertainty of (+/- 0.05 mm).  
• All quantitative measurements should have the same degree of precision, i.e. they should have the same number of significant digits.
• Graphs should show or include appropriate statistical tests and indicate what was used or conducted.  For example, if error bars are present, the graph should indicate what data or statistical test was used to create the error bars.
• Graphs should have labeled axes and title including units and uncertainties for each.

• Lab Drawings:

• Put effort into your lab drawings, they are an important part of qualitative data.
• Stephen Taylor has put together an excellent explanation of how to create drawings for IB that can be found here.

Evaluation

This criterion assesses the extent to which the student’s report provides evidence of evaluation of the investigation and the results with regard to the research question and the accepted scientific context.

Evaluation Further Explanation:

• Conclusion Justification

• Restate the hypothesis and expected results.
• One or more paragraphs that start with conclusions based on your experimental results and whether or not the experiment hypothesis was confirmed or denied; data must be used in your justification. If a specific hypothesis is not identified for the experiment, complete the same evaluation addressing the purpose/question of the lab.  In other words, sum up the evidence and explain observations, trends or patterns revealed by the data and using data.
• If comparing to a known or established value, your results should be compared to the known value using a correlation.  Citations should be provided for source information of known value.
• If applicable, concluding discussion should be related to additional examples or application beyond the scope of the individual lab.  If an article, scientific journal, etc. is used for this the source should be cited.
• Be aware of use of the terms “accurate” and “precise” in explaining and analyzing your data.

• Methodology Evaluation:

• The design and method of the investigation must be evaluated as well as the quality of the data by analyzing the degree of uncertainty and error in your results.  Explain how confident you are in the results and provide an explanation of your confidence using data from statistical analysis.  
• If using statistical analysis (which is pretty much always) discuss why you used the particular statistical analysis (standard deviation, t-test, correlation test, etc) and what this statistical test indicated about the experiment data.
• Identify and discuss significant errors and limitations that may have affected the outcome of the investigation.  This may include addressing whether there were variables not controlled for that may have affected results as well as problems with the procedure that may have made the investigation results unreliable.  This may also include the lack of repetition of multiple trials. These are systematic errors and should be the focus of this discussion.  Random errors such misreading of instruments should be highlighted and analyzed for their effect on the overall results, but should not be the primary focus of your evaluation.
• When discussing the systematic errors, discuss the significance of each in the impact on the overall results; it is appropriate to include a discussion of precision and accuracy during your evaluation.

• Methodology Improvement:

• Suggestions for improvement as based on the weaknesses identified in Aspect 2 should be addressed.
• Modifications to correct these weaknesses should be included and must be realistic means of correcting the problem; this can and should pertain to specific techniques or equipment used..
• Analysis and suggestions for additional/future experiments can be addressed here as based on the results.

• Bad Example (don’t do): “We should have worked more carefully”
• Good Example (DO!): “Rather than using a calorimeter to measure heat with a tin can, a styrofoam cup should be used instead due to it’s ability to retain and insulate heat rather than a tin can as it conducts and loses heat.”

Communication

This criterion assesses whether the investigation is presented and reported in a way that supports effective communication of the focus, process and outcomes

*For example, incorrect/missing labelling of graphs, tables, images; use of units, decimal places. For issues of

referencing and citations refer to the “Academic honesty” section.

Communication Further Explanation

• Statistics:

• Statistics can help to provide an idea or explanation to the accuracy of data, comparison of mean, significance between different data sets, and correlation between data sets.
• Calculations used to determine values (mean, standard deviation, t-test) can be conducted using graphing calculators or Excel, but an example of each calculation must be provided in the analysis
• Graphing Instructional Videos on Mr. Rott’s Website

• Combining & Manipulating Raw Data:

• Raw data is, well raw.  It may need to be combined or manipulated in order to analyze and be presented correctly.  This could mean taking the average of multiple measurements (very common), dividing, squaring, adding, subtracting, standard deviation, etc.  
• Data may sometimes be able to be analyzed and graphed without manipulation but this is rare.
• Manipulated data should be organized in a table and presented within the context of a report.

• Present Processed Data

• You are expected to be able to select the appropriate form of presenting your data on your own.  This could be a spreadsheet, table, graph, flowchart, diagram, etc.  Tables should have a title with clear headings and example calculations for any data processing.  Graphs will be similar with an appropriate title and labeled axes.  Units must also be included in whatever form of presentation is being used as well as uncertainty.  
• Example:

• Question: I know I want a graph to present my data, but which one?
• Answer:

• If you data is a percent or you are comparing a part to the whole = Pie Chart
• If both your independent variable data and dependent variable data is quantitative = Bar Graph
• If your independent variable data is continuously measured over a range (like measuring multiple solute concentrations ranging from 0 to 1) = Scatter Plot (probably with linear regression)
• If your independent variable data is clumped into groups (like measuring the height of all students in the school) = Histogram