Internal Assessment Lab Report Format

IB Biology Internal Assessment Lab Format

R. McGonegal – Palm Harbor University H.S.

The following titles and subtitles should be used for your lab report and given in

this order within your lab report.

Design

Question – must be focused and not ambiguous in any way

Hypothesis – state first & then give a logical rationale – your conclusion should

address the hypothesis you are giving here

Variables – chart or list identifying Independent, Dependent, & Controlled

Variables

Protocol Diagram – draw & label a diagram which best shows the major

protocol(s) you used. Often this will focus on the technique that was used to

measure the dependent variable and/or the technique that was used to ‘setup’

different increments of the independent variable. Make sure to show how

control group(s) differ from experimental group(s). This is also where I want you

to emphasize the inclusion of a period of time for ‘equilibration’ of equipment,

fluids, organisms, etc. The inclusion of time periods for equilibration should also

be included in your written procedure.

Photograph of Lab Setup – annotate this to show how variables were

instituted, especially the controlled variables. Do not just label equipment.

Procedure – write in paragraph form, passive voice, and past tense

Data Collection and Processing

Raw Data Table – make sure this is raw data only. Data table design & clarity

is important. A title should be given (Raw Data Table is not a data table title, it is

a lab report section title) Make sure that all columns, etc. are properly headed &

units are given. Forgetting one unit or misidentifying one unit is enough to drop

your score in this section. Do not “split” a data table (putting part of a table on

one page and finishing it on another). If you absolutely have to split a table (due

to quantity of data), make sure that you re-do the title and all column headings.

Uncertainties are mandatory and can be given within column headings for

equipment precision and as footnotes beneath data tables for other types of

uncertainties.

Data Processing

Overview – this is a short paragraph section that gives an overview of

how and why you decided to process and present the data in the form

that shows up later in this section.

Sample Calculation – neatly lay out and explain one example only of

any type of manipulation that was done to the raw data to help make it

more useful for interpretation.

Presentation – this is typically one or more data tables (of your now processed

data) and one or more graphs of this processed data. Once again, the design &

clarity of data table(s) is important and the quality of graphs is also very

important. Give careful consideration to the choice of graph style(s) that you

choose to do. Think about doing a scatter plot or perhaps a line graph showing

error bars or any number of other creative graphing styles rather than just a

simple line graph. Remember that demonstrating errors and uncertainties in your

data is also mandatory for the processed data. Make sure that you follow good

standard rules for doing graphs (valid title, axis’ labeled including units, etc.)

Note: Weak experimental design can sometimes limit you to pie graphs

and/or bar graphs; avoid this by good experimental design in which you

have a quantitative independent variable (with well chosen incremental

values) as well as a quantitative dependent variable.

Conclusion & Evaluation

Conclusion - this is a paragraph section in which you get a chance to discuss

the results of your experiment. Start by addressing whether your data seems to

support or refute your hypothesis. This should be discussed and not just stated.

Specifically refer to your graphs to give support to this discussion. Avoid the use

of the word “proof”or “proves” within your conclusion, as your data will not prove

anything.

Limitations of Experimental Design – this paragraph section discusses

how well your experimental design helped answer your experimental question.

What worked well (and why) and what did not work well (and why). This is also

a section in which outlier points could be discussed (if there were any outlier

points) as well as possible reasons for those outlier points. If you did any

statistical tests, what did the results of that test show? If you have error bars on

your graph(s) what do those show?

Suggestions for Improvement - In reference to the limitations given in the

previous subsection, what realistic and useful improvements could be made if

you were to do this investigation again?