Guide to Writing Lab Reports
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Published: Wed, 06 Jun 2018
Aspect 1: Defining the Problem and Selecting Variables:
Research Question (RQ)
The first part of planning an experiment is writing a good research question that you will investigate.
A Good RQ will:
- Include both dependent and independent variables
- Be Quantitative if appropriate
- Include the organism or tissue investigated
A hypothesis is a statement that addresses the RQ and makes a prediction about what will happen.
A Good Hypothesis will:
- Be written in an “If. . ., then. . ., because. . .” format.
- (If the [independent variable] [does something], then the [dependent variable] will [do something as a result], because [explanation].)
- Include both dependent and independent variables
- Be Quantitative if appropriate
- Be Testable (Falsifiable)
- Relate to the RQ
- Be explained
Variables are the different parts of your experiment that are able to change from one experiment to another. In order to perform a fair test it is important to make sure that we control as many variables as possible in order to gain accurate data.
A Good Variables list will:
- Include the Independent variable – the variable you change
- Include the Dependent variable – the variable that changes as a result of the independent variable
- Include other Controlled variables (constants?) and why we need to
- Identify the control (controlled variables are things we need to keep constant in each experiment)
These groups should be very clearly identified so that you may refer back to them throughout your lab report as you do data processing, data presentation, and your conclusion/ evaluation.
- Control group: This is the baseline group that you will be comparing the how the independent variable affects the dependent variable.
- This is NOT the same thing as controlled variables.
- Experimental group(s): This (These) is (are) the what is affected by the independent variable and is what you are measuring.
Aspect 2: Controlling Variables:
Control of Variables
Part of methods section of a lab is to include how you will control the variables, not simple what the variables are as listed above. It is possible to list the variables in the method section or to list them in their own section before materials and methods. If this is the case you will still need to discuss HOW you will control them in the methods section.
A Good Control of Variables section will:
- Specify how the measurements will be collected.
- Specify how the other variables will be controlled.
- Make sure that each variable in the list is mention
Aspect 3: Developing a Method for Collecting Data:
Apparatus and Materials
Includes the necessary equipment and materials to control and measure the variables listed in Aspect 1. Should be in its own section separate from Method.
A Good Apparatus and Materials List will:
- Indicate the correct materials for each variable
- Indicate the precision of measurements:
- ‘500 ml beaker’, instead of just ‘beaker’
- ‘Thermometer (0-100°)’ instead of just ‘Thermometer’
- ‘1 meter stick’ or ‘100 cm ruler’ not just ‘ruler’
- Can include an annotated diagram, but not necessary
Methods to Collect Sufficient and Relevant data
Includes a numbered series of steps to control all variables and collect sufficient and relevant data. It is important when planning an experiment to think about the RANGE and SIZE of measurements as well as how many REPLICATES of the experiment you will do. This is part of the methods section. Should be in its own section separate from Apparatus and Materials
A Good Methods section will:
- Include all steps necessary to complete the experiment (even the obvious ones- think about your grandma)
- Include how and when to take measurements or record observations
- Address an appropriate RANGE of intervals or measurements.
i.e. temperature from 0-10 or 0-100 or 50-100, etc
- IB requires that you have a minimum of 5 increments (or trials) with a minimum of 5 repeats at each trial.
Address the SIZE of intervals or measurements
i.e. what units of time will be used, or how long will the experiment run, etc
Indicates how many times the experiment will be REPLICATED
i.e. how many times should you do the experiment?
Makes sure that relevant data is able to be collected
Data Collection and Processing
Aspect 1: Recording Raw Data:
Collecting and recording raw data
Data collection skills are important in accurately recording observed events and are critical to scientific investigation. Data collection involves all quantitative or qualitative raw data. Qualitative data is defined as things being observed with more or less unaided senses (color, change of state, etc.) or rather crude estimates (hotter, colder, brighter, etc). Quantitative data involves some measurement.
A Good Data Collector will:
- Record all appropriate data
- Pay attention to detail
- Include units for all measurements
- Include uncertainties of the instruments used
Rules for data table construction
It is important when presenting data that is done in an effective and easy to read format. There are more than one ways to make a table, but you should always follow convention when making your tables.
A Good Data Table will have:
- A descriptive title
- Headings with units, no units in body of table
- Independent variable in the left hand column
- Dependent variable across the top
Uncertainties in all measurements
Whenever we make a measurement we do so with some error or uncertainty. We cannot make exact measurements, therefore it is important to indicate what level of uncertainty there may be. This should be done in the headings after the units are given.
Uncertainties are calculated as:
- ± ½ of the smallest unit measurable by the instrument. For example, a thermometer that is graded to 1°C has an uncertainty of ± 0.5°C
- ± 1 unit of length (½ x 2 measurements)
Aspect 2: Processing Raw Data:
Data processing means that you are actually converting the data into another form. Putting numbers into a table is not data processing!
A Good Data Processing section will:
- Show the formula you used, even if it seems simple
- Include processes such as:
- standard deviations
- % differences
- Statistical tests
- X2 (Chi-squared) test
Aspect 3: Presenting Processed Data:
Data presentation is not always necessary to every lab. You must evaluate if the data you collected is able to be graphed. [Hint: basically all quantitative data can be collected]
A Good Data Presentation section will:
- Use the appropriate graph type:
- continuous variable – best line or scatter graphs
- discontinuous variable – bar graphs
- parts of a whole – pie charts
- Have a descriptive title
- Have appropriate headings with units on both axis
- Be drawn neatly with axis being drawn in pencil
- Have clear labels or a key if more than one data set is present on one set of axis
- Have clearly marked and appropriate units
- Have points clearly located and marked
- NEVER ‘connect the dots’!!!
Aspect 1: Concluding:
A conclusion is not simply a restatement of the problem. It requires thought and analysis of the relevant data collected and presented.
A Good Conclusion will:
- Refer back to the RQ and hypothesis. Remember, you CAN NOT ‘prove’ your hypothesis right. You can support it, or disprove it, but you cannot prove anything!
- Be explained with reference to data analysis and literature values [translation: don’t say something that is not in your data!]
- Give the quantitative relationship between variables where appropriate – linear, exponential, inverse, positive, negative, not ‘it changed’, we can see that! Say how it changed!
- Compare results with text book or other literature values
Aspect 2: Evaluating the Procedure:
Most difficult part! You are not being judged as person, so don’t take the defensive and try and justify your mistakes! Be honest, and think hard about what you could have done better.
A Good Evaluation will:
- Identify sources of error in method and measurement
- Identify limitations in method [whether or not you chose it or not] and data collection
Aspect 3: Improving the Investigation:
After you identify possible sources of error in your experiment it is necessary to provide realistic methods to improve on your experiment.
A Good Improvements section will:
- Address each of the possible sources of error in the investigation and cite methods that could be used to fix them
- Change – don’t say the temperature changed, or the graph changed. Use increase or decrease, or another qualitative statement.
- ‘It’, ‘They’, ‘Them’ – use nouns. It doesn’t matter if you say the same thing 100 times! This is not English class.
- ‘Prove’ – You can’t prove anything. You can only support your hypothesis.
- SO. . . ‘The temperature changed, therefore it changed too, which proves my hypothesis to be correct.’ Is a horrible sentence!
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