Analysis

IB says: 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.

An overview of the suggested structure of this section is shown in Table 1. Details of what should be addressed in each subsection are presented after the table.

Here are some examples of Analysis sections.

 

Given the complexity and variability of this part of an investigation, we start with a Quick Reference Guide to allow you to quickly jump to any section you may need.

Quick Reference Guide

Use these links to jump directly to specific topics in this section

Data Tables Examples of Data Tables Figures Examples of Figures Sample Calculations Examples of Sample Calcs Uncertainties: Measured and Calculated Graph Presentation Curve-fitting on Graphs Measurements from a Microscope Drawing Standard Deviation and Tests of Statistical Significance (Biological Sciences)

How-To Videos (Links will open in a new window) Insert a figure into a document Enter data into Excel Use Excel to process the data Present your raw data graph Present your final manipulated graph

 

The ANALYSIS section of a report should include three subsections: 

1. Data: Recording the raw data.
2. Data Processing: Processing the data.
3. Results & Interpretation: Presenting and interpreting the results of the processed data.

 

Table 1 Overview of suggested structure of this section with the IB Criteria addressed in each part.

Section 2:  ANALYSIS	IB Criteria
Subtitle: Data	Analysis The report includes sufficient relevant quantitative and qualitative raw data that could support a detailed and valid conclusion to the research question.
Quantitative data, including uncertainties, is presented fully and appropriately Qualitative data is presented fully and appropriately (if relevant)
Subtitle: Data Processing	Appropriate and sufficient data processing is carried out with the accuracy required to enable a conclusion to the research question to be drawn that is fully consistent with the experimental data. The report shows evidence of full and appropriate consideration of the impact of measurement uncertainty on the analysis.
All manipulation and processing of data needed is clearly shown, including: Samples of all calculations performed Samples of all uncertainty calculations performed Samples of how data was obtained from computer-generated graphs, if appropriate
Subtitle:  Results & Interpretation	The processed data is correctly interpreted so that a completely valid and detailed conclusion to the research question can be deduced.
Processed data results are presented appropriately (graph, table, figure…) Results are interpreted to enable a conclusion addressing the RQ

Sub-subtitles mirroring IB Criteria (ie Raw Data, Quantitative/Qualitative Data, Sample Calculations, Results, Interpretation) are suggested, as appropriate.

 

Subsection 1: Data

In the Data subsection your goal is to communicate clearly all data obtained during the investigation.

Data may include:

• tables with measurements and readings,
• diagrams,
• descriptions or
• photographs.

All data must be clearly labeled and easy to understand.

The way that data is presented depends on the type of data:

• Quantitative data need to be in S.I. ("metric") units and must be consistent throughout. 
  • Any uncertainties in the data must be clearly shown.
• Diagrams need to be clear and large enough to see details.
  • Biology diagrams must be in pencil.
• Descriptions should be precise and informative:
  • Was the solution after the test "blue" or was it "clear, deep, brilliant royal blue"? Or, maybe it was "cloudy, greenish blue"?
  • Carefully describe how things felt, looked, smelled, or sounded.

It is a good practice to keep a raw data sheet during investigations for

• descriptive observations,
• collection of data, and
• weaknesses/improvements noted in the procedure. 

Your teachers will help you with the details of how to do all of these things for the laboratory investigations that you will be doing in your classes.

Data Tables

Tables require an explanatory title. The title will generally include the variables being graphed.

Here are some examples of good table titles:

Table 1: The Acceleration of a Cart as Mass is Varied
Table 2: Rate of Oxygen Production vs. Enzyme Concentration
Table 3: Number of Seeds Germinating as pH Levels are Varied

Tables also need a label and a caption, written below the table, that explain important points to be noted.

Tables and figures are refered to in the text by the numbered label, i.e. "The data in Table 1 clearly show that as more force is applied the acceleration of the cart is increased".

Rules for Tables

• All tables must have:
  • a title
  • a numbered label and a caption
    • the caption should explain
      • important points,
      • symbols,
      • abbreviations, or
      • special methods.
• Label column headings with the quantity being measured, the units of measurement, and the uncertainty of the measurement.
  • Place units in the headings -- not in the individual cells of the table.
  • Uncertainties are estimates and should be rounded to one significant figure ( ± 0.34 should be rounded to ± 0.3). *Note: Standard Deviation may be rounded to 2 significant figures.
  • The data in table cells must be rounded to match the uncertainty ( 14.37 ±  0.3 should be rounded to 14.4 ±  0.3).

More Suggestions for good Tables

• Generally, the independent variable should be in the first column, with subsequent columns showing the results for the dependent variable
• It is good practice to center the data in the columns and the titles of the columns over each other.
• Including some processed data such as averages in a raw data table is acceptable.
• The reader should be able to understand tables without referring to the text.
• Avoid having tables that are not referred to in the report.
• NEVER split data tables over two pages. Titles must be on the same page as the table.

To summarize, a table needs a title; label and caption; quantities, units and uncertainties for each column; and clear organization and formatting.

Data Tables Here are some examples of Data Tables.

Figures

Some types of data are more appropriately presented in a figure.

This includes data and observations in the form of:

• photographs,
• drawings,
• diagrams and
• graphs.

Figures include:

• photographs or diagrams of experimental set-ups;
• drawings of plants, cells, or microbes;
• photographs or diagrams of experimental results; and
• computer-generated graphs showing collected data.

Suggestions for good Drawings

• All original drawings should be done in pencil.
• Drawings must be large enough to show clear detail.
• Stippling (drawing with short strokes or dots) on biological diagrams is better than shading.
• Drawings should be done on unlined paper.
• Drawings should be realistic.
• Composite diagrams are acceptable.
  

Rules for Figures

• Figures must be clear and easy to understand.
• All Figures must have a numbered label and caption placed below the figure. The caption must
  • describe the figure,
  • explain important points, such as:
    • a legend (key)
    • any unusual symbols,
    • abbreviations, or
    • special methods used.

Here are some examples of good labels and captions for Figures:

Figure 1: Bean Seedlings after 1 Week of Growth. The seedlings now have fully developed leaves and root systems.
Figure 2: Onion Epidermal Cells Stained with Iodine
Figure 3: Results of Sugar Tests on Unknown Samples. Note that the result for the last sample is significantly lower than the rest.
Figure 4: The velocity-time graph for the ball thrown in the air in trial 1. The slope of the graph is the acceleration of the ball due to gravity.

Figures Here are some examples of figures.

 

Subsection 2: Data Processing

This section of a lab research report should show how you used your raw data to answer your research question. Often, the measurements that you make during the experiment cannot directly provide an answer to your research question, so you need to perform some calculations using the raw data. The equations you use and the ways you rearrange the data will depend on the investigation. You will be learning how to process data appropriately in lab investigations throughout the year.

Special Data Processing Techniques for Biology Investigations

For continuous data in biology investigations, a t-test (available at graphpad.com) is used to determine if the independent variable had a significant effect on the dependent variable. 

For categorical data, use a chi-square test to do the same.

For a tutorial on how to choose and use the appropriate test, go here.

Sample Calculations

One example of all significant calculations or manipulations performed on the raw data should be shown in a subsection entitled “Sample Calculations”. Sample Calculations should be shown for one typical data value.  The trial or value used should be clearly stated. Sample Calculations may be divided into several sections if this helps the reader follow the developing argument.

Sample Calculations start with the equation used to process the data.  The appropriate values with units are then substituted into the equation.  Finally the result of the calculation should be shown.  The calculation of the uncertainty in your processed data should also be shown, where appropriate.

It is generally appropriate to present the final processed data in a table at the end of this section, prior to graphing in the Results section

In cases where your data were collected using a computer, it is usually appropriate to include a sample graph showing your reader how the data was derived from the computer-generated graph.  Use the caption to explain how your data were derived from the graph (for example: slope, average, or y-intercept) and to identify which trial this graph is from.  Follow the rules for presenting graphs given below.
 
Uncertainties Here are the general rules on how to determine and report on uncertainties in processed data.

Sample Calculations Here is an example of what a good Sample Calculations section looks like.

 

Subsection 3: Results & Interpretation

Results

You must present the processed data in a way that clearly answers your research question. Your processed data can usually be presented in a line graph, table, or diagram. Bar graphs or pie charts are also sometimes appropriate. Knowing the most appropriate way to present various types of results is one of the things you will be learning in your classes.

Graphs

Graphs are often the most appropriate way to present your results.  We will be using the Logger Pro graphing program for making graphs (some students may opt to use Microsoft Excel). A tutorial on Logger Pro has been included in this writing guide. Use it.

A good graph has certain characteristics.

Rules for Graphs

• All graphs must have:
  • a title telling what the graph is showing. A good title usually includes the independent and dependent variables.
  • Each axis must have a label and units, telling
    • what is being measured and
    • the units of measurement.
  • Uncertainty bars should be included, when appropriate. 
  • A best-fit curve should be fitted to the data, when appropriate, rather than connecting the dots.
    • Note: Lines of best fit are only appropriate if there is good reason to believe that intermediate points fall on the line between two data points.
    • Note for BIOLOGY graphs: You MAY NOT have the computer-generated best-fit curve GO BEYOND THE ENDS OF THE DATA. If you are using LoggerPro to graph your data, you will need to manipulate the image to cover the extrapolated parts of the best-fit curves that LoggerPro always shows.
  • A caption describing:
    • what is being shown on the graph,
    • discussing any important points that the reader should notice.  These might include:
      • the meaning of the slope and intercepts,
      • any interesting features in the data. 

More Suggestions for good Graphs

• The independent variable should go on the x-axis, and the dependent on the y-axis. 
• The axes of the graph should be scaled appropriately, so that the data fills most of the space in the graph. A good rule of thumb is that the data should take up approximately 75% of the space in the graph.
• Graphs usuallly start from the origin (0,0) but this is not a requirement. You must first ensure that this is appropriate for your particular data set, and that it allows you to meet the previous criteria of the data filling 75% of the graph. 
• For Physics, it is best if the final graph is manipulated to produce a straight line fit. This allows you to determine values for the slope and intercepts, which are usually meaningful.
• It is often appropriate to have several graphs showing different aspects of your results.
• The type of graph chosen (scatterplot, line, bar, etc.) should be appropriate to the nature of the data collected.

Curve Fits Here is more in-depth information on how to choose and use a curve fit appropriate to your data.

Graph Presentation is an example of how to present your results graphically.

 

Interpretation

You are required to include an "interpretation” of your results in the Analysis section. It is not enough to just show your final graph or table and stop there. From the IB Guide, interpretation is the following: “Use knowledge and understanding to recognize trends and draw conclusions from given information.”

Your interpretation must directly address the research question. Your interpretation should identify the trend/relationship shown by the graph/data, along with any other relevant aspect of the results, such as the values of slopes or intercepts. This should be done after each graph and/or data table.

The interpretation is similar to your conclusion, however your conclusion will include more discussion of how the experiment supports/doesn’t support your original hypothesis and how/if the experiment is supported by scientific theory.

A good interpretation will:

• Relate to the research question.
• Simply state the trend observed in the graph.
  • Include an interpretation of the slope and intercepts, if relevant.
• State the trend observed in EACH graph separately, immediately following the graph.
• For labs without graphs, the interpretation should occur after each set of processed data.
• Note: No attempt should be made in the interpretation section to explain the trend. That will be done in the conclusion section.

 

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