1. Steps in carrying out a scientific investigation

2. The Scientific method Making observations Forming hypotheses
Generate a null hypothesis
Make predictions
Test predictions (method/collecting and processing results)
Accept or reject hypothesis (conclusion/discussion)

3. Making observations
Involves what animals are doing in particular situations– why are they doing that?
For our crab investigation you will be doing transects of the intertidal zone – where are they located (high, mid. Lower part of the zone), in what numbers, what part of the environment were they found (shelters, what heights, substrate), how many other organisms are found in their habitat, what kinds of seaweed could be found etc.

4. Forming an hypothesis
Generally, a purpose is a hypothesis and should be written as such.
A hypothesis is a prediction statement precisely detailing the expected outcome of the investigation: If this happens then ‘that is predicted to happen’.
A hypothesis must be easily testable by experimentation and it should be obvious what needs to be manipulated and what needs to be measured.
It must be very specific and based on both the dependent and the independent variables.

5. The null hypothesis
The null hypothesis (H 0) is a hypothesis which the researcher tries to disprove, reject or nullify.
The null hypothesis says there is no statistical significance between the two variables.
It is always opposite to the ‘alternate’ hypothesis.

6. Examples of hypotheses
A null hypothesis (H0) exists when a researcher believes there is no relationship between the two variables. This is something to attempt to disprove or discredit.
eg There is no significant change in my health during the times when I drink green tea only or root beer only.

7. Examples of hypotheses
This is where the alternative hypothesis (H1) enters the scene. In an attempt to disprove a null hypothesis, researchers will seek to discover an alternative hypothesis.
eg My health improves during the times when I drink green tea only, as opposed to root beer only.

8. Your hypothesis for the animal study
MILESTONE 1: Formulate possible hypotheses and consider how they could be tested with the time and equipment available to you. You can ask your teacher to supply the equipment that you will need for your investigation. Discuss your hypotheses and ideas with your teacher. After discussion with your teacher choose one hypothesis to investigate (explicit and testable) linked to a scientific/biological concept or idea. MILESTONE 1 DUE DATE: Thursday 27 July (to be approved and signed off in your logbook) Task: Try generating some ideas as to what your hypothesis might look. Is it testable?

9. Designing a method When designing a method, keep two things in mind:
Keep it simple – a good method must be able to be followed by someone else, and yield straightforward results (to interpret and analyse).
Ensure you are very clear about what is being measured and what must be manipulated to get these measurements.
Be prepared to do some trialling of your original method and make necessary changes.

10. Characteristics of a valid method Variables
The independent variable
The one variable that is manipulated.
It must have a range (a minimum of 3).
The range must be valid (suitable for the organism being studied).
The range should be established through trialling.
eg If using a range of salt solutions, some of the most concentrated may lead to the death of the organism, so will be outside the valid range.

11. The dependent variable
This is the variable that will change, as it depends upon the independent variable.
Changes need to be measured or sampled in some way.
Be very specific about what the dependent variable is and how it will be measured.
eg In animal behaviour experiments you cannot measure ‘what it likes’ so some way must be found for objectifying these qualities, such as counting how many animals are within each category for the range.

12. Controlled variables
These are all the other variables that must remain constant for a fair test.
They must be identified, and how they were controlled must be specified.
They include details such as amount and type of substrate to use, how ambient (environmental) temperature was controlled, how light intensity was controlled etc.
Inadequate or incorrect control of these variables may mean an investigation is biased and the results may not be valid.

13. Assumptions
These are variables outside of your control despite your best efforts, or you can justify that they will make little or no difference to the outcome of the investigation.
eg genetic variation within individuals of a species (cannot be controlled, but can take steps to minimilise effect such as having a large enough sample size).

14. Sufficiency of data
There are several ways of ensuring sufficient data is collected. The way(s) chosen will depend on:
Constraints imposed by practical issues (eg equipment or time available).
The amount of variation between individuals and the nature of the organisms being investigated – if there is a high degree of variation, it is more correct to have a large sample size than lots of replicates of a small sample size.
Establishing sufficiency is something you should do as part of your trialling. Record all the results of these trials, so sufficiency decisions can later e justified.

15. Precision, accuracy and reliability
Obtaining precise and accurate data are tests of the soundness of the method used.
Precision refers to the reproducibility of the data – how close measurements are to each other in value.
Correct experimental techniques, accurate instruments, low personal error and correct sampling methods all contribute to high precision of collected data.

16. Accurate data are those where the measured or derived values are close to their true value.
Investigations that have been set up to be unbiased give accurate data.
Think of precision and accuracy in terms of throwing darts on a dartboard – if 10 darts hit the bullseye, you are both accurate (the darts have hit the intended target) and precise (the darts are vey close together on the dartboard).

17. Bias
Data obtained must be a result of the true response of the organisms because of the independent variable.
A biased investigation is one where the data obtained could reflect the true response of the organisms to the independent variable or could result from poor experimental design or technique.
A fundamental premise of statistical analysis is that all variation is random. If an experiment is biased in any way, then this condition is not met.

18. Examples of bias
Bias usually results from one or more of the following:
Not having a truly random sample.
Generalising from a non-random sample (can have specific requirements for your organism, eg size, gender etc, as long as you can justify your reasons, but incorrect to attempt to apply general statements to all organisms when you haven’t used a random sample).

19. More examples of bias
Non-random allocation of subjects to treatments. Using random numbers is a good way to overcome this.
Biased experimental technique. Can be simple human error (eg measuring technique gets better as the experiment progresses).
Not vigorously identifying and controlling all variables other than the independent variable (eg light intensity – also produces heat).

20. Measuring or sampling
An essential part of the method is exactly what data will be collected and how it will be collected.
Detail must be specified on:
What will be measured
When it will be measured
What the unit of measurement will be
How it will be measured
How the raw data will be processed
Be sure to record all raw data in the logbook.

21. Method structure
A good method is so well structured than an independent investigator could follow it without needing further clarification.
Methods can be written as a series of numbered steps, or as paragraphs, but must be in a logical sequence of events.
Diagrams are useful to convey information in methods. Follow the guidelines for drawing scientific diagrams.
Avoid repetition of the same instruction for replicates.
Ask someone (preferably a non-scientist) to proof-read for you. If they can understand it, you’re on the right track.

22. Collecting, recording and processing data
Collecting data
Data should be collected as soon as you begin your investigation.
Observations of behaviour while in containment are data.
Measurements of abiotic factors are data.
Record all data in your logbook, including date, time, units, etc in a retrievable format.

23. Recording data
Raw data – this is unprocessed data recorded in your logbook.
Raw data should never be part of the main body of your report.
Raw data are normally presented in a table or (tally) chart format with the following features:
A full and informative title
Each column and/or row has a full and informative heading, including correct units
The table or chart is fully enclosed with neatly ruled lines

24. Processed data
Data needs to be processed by calculating means and/or standard deviations.
These need to be recorded in a table or chart in the results section of the report.
The processed data are used to draw an appropriate graph(s) to illustrate a pattern or trend (or its absence).

25. Processed data – examples of graphs
Line graphs
Used to show continuous data and show how one quantity is affected by another.
Ensure the independent variable is in the horizontal (x) axis.
A problem often encountered is whether to join the points up or draw a line of best fit.
When measurements are suspected to be subject to appreciable error (either in measurements themselves or in the experimental technique) it is best to draw the line of best fit.

26. Bar charts
Used when there are no intermediates, either because a variable is discrete or because is cannot be quantified.
Bars are separate when there don’t have intermediate values eg egg size, species

27. Scattergrams
Used to show whether two variables are correlated – in other words, if the value of one is an indication of the value of the other.
Whether variables are related depends on the way the points are clustered:
Positive correlation: scattered upward
Negative correlation: scattered downward
No correlation: randomly scattered
The stronger the correlation, the more dots tend to be clustered in a line.

28. Histograms
Used to show the relative frequency of different measurements and is used only with continuous data.
When plotting a histogram, measurements are divided into classes.
The columns touch.

29. Kite diagrams
Used to show how the abundance of an organism changes along a chosen line or transect.
The dependent variable (some measure of abundance) is plotted as two symmetrical lines, one on each side of a central axis.

30. Pie charts
Used to indicate the percentages of various constituents of a whole.

31. Chi-squared test
Used to find out whether variation is due to chance, or to do with one of the variables you are testing.
The point is to be able to accept or reject the null hypothesis.
The null hypothesis states that there is no significant difference between the observed and expected frequencies.
Data is measured to see if there is a significant difference using the following equation:
When we conduct a χ2 test, we compare the observed frequencies in each response category to the frequencies we would expect if the null hypothesis were true.
Watch the link below to work
out what all the symbols mean.
Chi-squared test bozeman

32. Critical value and degrees of freedom
The critical value is the significance level required for a particular test (usually 95% -> p=0.05 in biology).
We find the critical value in a table of probabilities for the chi-square distribution with degrees of freedom (df) = k-1.
Degrees of freedom, or df, is calculated by multiplying the number of rows minus 1, by the number of columns minus 1.
df = (rows – 1) x (columns – 1)
For a 2x2 table that is:
(2 – 1) x (2 – 1) = 1
Once you have your critical value (0.05), degrees of freedom, and your results from the chi-squared test, you can compare your results with the table of critical values.

33. Table of critical values

34. What the data means Are your results significant?
If p≤ > statistically significant
If p> > not statistically significant
To recap:
Chi-square test, by hand
Simple explanation of Chi square test

35. Writing your report
The report must be presented as a written document that uses correct scientific terminology, including common and scientific (binomial) names for the species investigated.
Footnote any sources of information used and site all sources in a reference list at the end of the report.
If using a quote, use quotation marks, and footnote the source of your information.

36. What to include in your report
Purpose
Written as a hypothesis linked to a biological concept/idea, with its opposing null hypothesis.
Method
Your final method as a result of your trialling.
Results
All raw data (observations, measurements, samples, statistical tables, calculations) – in logbook.
Only processed data in your report (averages, means, graphs, results of statistical analysis).
The results section should also include an interpretation of the processed data.
Relevant findings/results from other sources (eg other students) need to go into an appendix.

37. Conclusion
A short paragraph based on the interpretation of the processed data, stating the outcome of the investigation.
It must identify the relationship between the dependent and independent variables and refer back to the stated hypothesis.
The conclusion is not the place to:
(re)state or describe results
Discuss the results in terms of the biology of the organism
Make assumptions or give explanations that are untested, ie that you have no evidence to support

38. Discussion
The discussion needs to explain the biological ideas or concepts relevant to the investigation.
The explanations need to account for your findings as well as those from other sources – such as scientists or other students.
Findings obtained from scientific principles, theories and models may also be used.
References are used in writing a discussion.
You need to do more than just describe or reiterate your results. You must explain how the results are significant in terms of biology/biological relationships of the organism being studies.
The results/findings of others are used to support your findings.
The discussion also needs to include a justification.

39. Justification
You need to justify the choices you made in your investigation by evaluating the following:
The validity of the method – did the method allow measurements of what you wanted without bias or error. Was it a fair test? Were all variables controlled/accounted for? Account for any changes made from trialling, thus justifying the final method used.
The reliability of the results – was sufficient data collected so that a trend/pattern (or absence) was clearly shown? Statistical analysis (eg chi-squared test) may be used as part of this evaluation.