1. Advanced Higher Biology Unit 3 Investigative Biology

2. 3 Critical evaluation of biological research (c) Evaluating data analysis.

3. SQA

4. Success criteria  Work out Standard Deviation, Mean and standard error of a data set.  Draw error bars on a graph  Carry out a t-test on a data set

5. Evaluating the reliability of an experimental design  With repeated measurements of a variable in a biological investigation it is very unlikely that you will consistently obtain exactly the same values.  This may be due to confounding variables or a small amount of measurement error.  Biological systems are complex. As a result of this there will always be some variation in your results.

6. Can’t think of another title...  To get an idea of how varied your data are, and therefore the reliability of your experimental method the data can be analysed using descriptive statistics.  Descriptive statistics can provide a measure of the variability of a data set – to make a comparison between replicates.  The variability can be presented as a number or displayed on a graph of the data as an error bar.

7. Standard Deviation  The standard deviation is one of the most commonly used measures of data variability.  How much does each measurement differ from the mean of the data set.  The larger the standard deviation of a data set the more variable the data.

8. Significant difference?  In biological investigations you will often make a comparison between the results obtained from a control group and an experimental group in which you have systematically altered one specific variable.  Once the results for each condition have been obtained they can be analysed to establish whether or not they are significantly different from each other.

9. Statistically different!  “A difference is statistically significant if there is a very low probability of it occurring by chance if there really is no systematic difference between the two groups. The cut-off probability most commonly used in research science is 5%. This is called the 0.05 significance level.”

10. Significantly different!!!  If the difference is found to be statistically significant then the null hypothesis can be rejected.  Providing the experiment has been designed to effectively control the confounding variables then it can be concluded that the single factor by which the data sets differ is likely to be causing the difference between the results.

11. The t test  The t test is used to check for a significant difference between two data sets.  The mean and the standard error of the mean of the data sets are used to calculate a number called a t statistic. The greater the value of the t statistic the more likely the difference between the results is significant? Check…  The t- test in Excel gives the probability (p value) and if p<0.05 the difference is significant.

12. Muller-Lyer illusion

13. Protocol 1. The tester should place the apparatus on the bench in the horizontal position. Slide the adjuster to the longest position. 2. The subject should slide the adjustable arrow until the lengths of the two arrows appear to be equal. 3. The tester should read the reverse to note the discrepancy on the ruler. 4. Each time the subject completes the task, the arrow should be adjusted to its full length. 5. Repeat 5 times. 6. Swap roles. 7. The tester should now place the apparatus on the bench in the vertical position. Slide the adjuster to the longest position. 8. Repeat steps 2-5

14. Results Horizontal error (mm)Vertical error (mm) 1 2 3 4 5 6 7 8 9 10 Mean

15. Success criteria  Work out Standard Deviation, Mean and standard error of a data set.  Draw error bars on a graph  Carry out a t-test on a data set