Old Standards, don’t use

Design

The design portion of experiment focuses on the development of a testable hypothesis that controls for and identifies variables with a procedure to test the hypothesis.

Aspect 1: Defining the Problem & Selecting Variables

• 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.
• 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 (independent), those that will respond to manipulations (dependent), and those that are not manipulated (control) must be identified.  Additionally all aspects of the experiment that must be maintained throughout the experiment (constants) must be identified.
• Hypothesis(es): Most labs will require a specific hypothesis.  This 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.

Aspect 2: Controlling Variables

• The controlling of variables is imperative to the successful of an experiment.  This means that 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.

Aspect 3: Developing a Method for Collection of Data

• Materials:

• Create a list of experimental materials/apparatus.  Be specific.
• A description of how the experiment will be set up; this should be supplemented by diagrams, sketches, or photos to illustrate the assembled.
• If selecting a quantity to use during the course of the experiment, justify the quantity you select.

• Method & Procedure:

• List the means that the experiment will be conducted using a detailed 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.
• If an action is completed to minimize anticipated error, discuss this as well.
• Clearly state how data will be collected and what anticipated qualitative observations you anticipate.

• 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, “Five by Five Rule.”
• 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.

Data Collection & Processing

The primary purpose of Data Collection & Processing is to, surprise, collect and process raw data followed by a presentation of that data in order for it to be easily interpreted.  

Aspect 1: Recording Raw Data

• 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.

• Tables & Measurements:

• Give a specific, identifying title to each data table (see example below).  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.  This a ruler would have an uncertainty of (+/- 0.05 cm).  Generally it is also safe to use (+/- 0.5 units) for analogue measurements and (+/- 0.05 units) for digital.
• All quantitative measurements should have the same degree of precision, i.e. they should have the same number of significant digits.

• 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.

Aspect 2: Processing Raw Data

• 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, etc.  
• Data may sometimes be able to be analyzed and graphed without manipulation but this is rare.

• 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

• Aspect 3: 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 calculations.  Graphs will be similar with an appropriate title and labeled axes.  Units must also be included in whatever form of presentation is being used.  
• 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

• Making Graphs in Excel Videos:

• Pie Chart
• Bar Graph
• Scatter Plot
• Histogram

Conclusion & Evaluation                

The purpose of the Conclusion & Evaluation section is to provide an explanation of your results and analysis of both their significance and of investigation errors and uncertainties.  The section should also include a discussion of means to improve the experiment as based on the identified errors and uncertainties.

Aspect 1: Concluding

• 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.

Aspect 2: Evaluating

• 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.

Aspect 3: Improving the Investigation

• 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.”