1. Investigations using the
Müller-Lyer Illusion
Kate Andrews
Lorraine Bruce

2. Plan and design investigations using the Müller-Lyer illusion.
Unit 3 Neurobiology and Communication
Plan and design investigations using the Müller-Lyer illusion.

3. Aim:
To consider the use of the Müller-Lyer Investigation as a model of investigative design which might support some of the suggested learning activities, approaches and skills required for Advanced Higher Biology, Unit 3.

4. What is the Muller-Lyer illusion?

5. Other geometric illusions
The Müller-Lyer illusion

6. 3-D versions
Other investigations:
No tails / fins

7. How might we measure the apparent extent of the Mϋller-Lyer illusion?
An adjustable figure......

8. With a scale!!!

10. Skills of scientific experimentation, investigation and enquiry
Through practical work candidates develop a deeper understanding of biological knowledge and acquire skills of:

11. 3. Processing information / calculations....
4. Planning and designing / hypotheses / identification of variables / controls.....
5. Evaluating experimental procedures.....
Drawing valid conclusions / evidence / justification....
connection between variables and controls.

12. Unit 3 Investigative Biology
BIOLOGY (revised)
Outcome 1 and Outcome 2
1 Scientific principles and process
(a) Scientific method
(b) Scientific literature and communication
(c) Scientific ethics
Advanced Higher
Unit 3 Investigative Biology
3 Critical evaluation of biological research
(a) Evaluating background information
(b) Evaluating experimental design
(c) Evaluating data analysis
(d) Evaluating conclusions
2 Experimentation
(a) Pilot study
(b) Variables
(c) Experimental design
(d) Controls
(e) Sampling
(f) Ensuring reliability
Outcome 3
Carry out a biological investigation.

13. It is envisaged that learners could cover the above content in a manner that is integrated across the other Units of the Course and based upon an appropriate mixture of practical work and stimulus material derived from scientific publications.

14. Failure to find an effect (i. e
Failure to find an effect (i.e. a negative result) is a valid finding, as long as an experiment is well designed.
Scientific method
The null hypothesis can be used in the design of experiments to investigate a possible effect.
Scientific ethics
In human studies, informed consent, the right to withdraw data and confidentiality are important considerations.
..a pilot study is used to help plan procedures, assess validity and check techniques. This allows evaluation and modification of experimental design.
Pilot study

15. Variables can be discrete or continuous...
....other variables besides the independent variable may affect the dependent variable. These confounding variables must be held constant if possible......
In random sampling, members of the population have an equal chance of being selected.
Sampling
The extent of the natural variation within a population determines the appropriate sample size.

16. Overall results can only be considered reliable if they can be achieved consistently.
Ensuring
reliability

17. Measuring the apparent extent of the illusion
What conditions affect the apparent extent, or size, of the illusion?
..make no theoretical assumptions..
..set out to determine whether or not a particular independent variable has any effect on the size of the illusion

18. Variables? ***********
..compare the effects of presenting the illusion either horizontally or vertically..
Independent variable – orientation
Dependent variable – apparent size of the illusion
***********
Orientation:

19. Other decisions about the method
How will we measure the extent of the illusion?
How many subjects?
How many readings under each of the two conditions?
Between, or within, subjects design?
Random selection of the group....

20. The testing situation
The distance between the subject and the Müller-Lyer figure....
The adjustable part of the figure ….
How long the subject is allowed to take to adjust the figure....

21. How the subject will be instructed
presented with the illusion figure, the task is simply to adjust the figure until the two parts appear to be of equal length.
….... judge the apparent equality of the two parts of the figure, do not to try to ‘compensate’ for the illusion..

22. Null hypothesis - the orientation will have no effect on the apparent extent of the illusion.

23. Evaluating data analysis
Statistical tests are used to determine whether the results are likely or unlikely to have occurred by chance. A statistically significant result is one that is unlikely to be due to chance alone.
Use a statistical test to confirm or refute significance of results.....

24. Carry out the investigation in pairs.
One person is the ‘tester’, one the ‘subject’.
20 measurements – horizontal presentation.
Swap roles. 20 measurements - vertical presentation.
Tester record results on a spread sheet.

25. Calculate the average (mean) for each set of data
Click on the empty cell below the final number in the column and then on more functions, statistical and then average.

26. Write your horizontal and vertical means (averages) on the board.
This gives the group data set for the t-test.

27. The Excel t-test works out the mean for you.
The t-test is used to find out if there are significant differences between the means of two sets of numbers.
The Excel t-test works out the mean for you.
Because we can’t predict the direction of the results i.e. we have no reason to expect that the apparent extent of the illusion will be greater for the horizontal condition, or for the vertical, it is a ‘two-tailed’ test.
Because our design is a between-subjects design, the appropriate test is the ‘unrelated’ t-test. In Excel’s t-test this is
‘two-sample, equal variance’

28. Using Microsoft EXCEL Open up a new spread sheet.
Enter the horizontal mean deviations into column A and vertical mean deviations into column B.

29. t-test
more functions
statistical
TTEST

30. It is a two tailed test, type 2

31. Check significant figures - Right click on answer cell – adjust to appropriate significant figure

32. Excel’s t-test gives the probability (p value).
If p < 0.05 the difference is statistically significant.
(For other types of t-test you may have to work out ‘degrees of freedom’ – two less than the total number of individual measurements in the two samples - and then consult a table of t values.)
In either case, if the result is statistically significant you reject the null hypothesis.
If the result is not statistically significant you cannot reject the null hypothesis.....