1. Statistical Analysis
WHY ?

2. Quantitative Data
Quantitative – measured using a naturally occurring numerical scale
Examples
Chemical concentration
Temperature
Length
Weight…etc.
4/5/2017

3. Qualitative Data
Information that relates to characteristics or description (observable qualities)
Information is often grouped by descriptive category
Examples
Species of plant
Type of insect
Shades of color
Rank of flavor in taste testing
Remember: qualitative data can be “scored” and evaluated numerically
4/5/2017

4. Sampling Data
Don’t have enough time or resources to measure every individual in a population.
Choose and measure a representative sample from a population.
Need to have a good SAMPLE SIZE in order to “believe” your data. (statistically significant)
4/5/2017

5. Can you count EVERY ONE
4/5/2017

6. Displaying the data
Error bars can be added to graphs to show the range of data.
This shows the highest and lowest values of the data.

7. Mean Another word for the average
Calculated by summing the values and then dividing by the number of values obtained.
Symbol: x

8. Statistical analysis of a sample
Mean: is the average of data points
Range: range is the measure of the spread of data
Standard Deviation: is a measure of how the individual observation of data set are dispersed or spread out around the mean
4/5/2017

9. What does the standard deviation measure
The standard deviation measures how spread out your values are.
If the standard deviation is small, the values are close together.
If the standard deviation is large, the values are spread out.
It is measured in the same units as the original data.

10. Standard deviation Measures the spread of data around the mean.
Formula: s = √(x - x )2
BUT you do not need to remember it.
You must be able to calculate it on your calculators (or spreadsheet in the lab)

11. Standard Deviation
The standard deviation tells us how tightly the data points are clustered together
When standard deviation is small—data points are clustered very close
When standard deviation is large—data points are spread out
4/5/2017

12. Standard Deviation
We will use standard deviation to summarize the spread of values around the mean and to compare the means and spread of data between two or more sample
In a normal distribution, about 68% of all values lie within ±1 standard deviation of the mean
This rises to about 95% for ±2 standard deviation from the mean
4/5/2017

13. 4/5/2017

15. ±1s (red), ±2s (green), ±3s (blue)

16. Why is it useful? Calculate the mean of 100, 200, 300, 400, 500.
Now let's imagine you had the values 298, 299, 300, 301, 302. Calculate the mean of these numbers.
Although the two means are the same, the original data are very different.

17. The standard deviation will reflect this difference.
The standard deviation of 100, 200, 300, 400, 500 is 141.4
The standard deviation of 298, 299, 300, 301, 302 is
So the standard deviation of the first set of values is 100 times as big - these data are 100 times more spread out.

18. 4/5/2017

19. Error Bars
To graphically display data, you will use the CI to generate error bars.
Error bars represent the spread around the mean.
4/5/2017

20. Comparing Means -
What can you conclude when error bars do overlap? When error bars overlap, you can be sure the difference between the two means is not statistically significant. (Due to chance variations)
What can you conclude when error bars do not overlap? When error bars do not overlap, you cannot be sure that the difference between two means is statistically significant. T-test is commonly used to compare these groups.
4/5/2017

21. 41.6

22. 41.6
45.9

23. Comparing the two
41.6
41.6
45.9

24. Why do I need both the mean and standard deviation?
Although the standard deviation tells you about how spread out the values are, it doesn't actually tell you about the size of them.
For example, the data 1,2,3,4,5 have the same standard deviation as the data 298,299, 300,301,302

25. Displaying the data
Error bars can be added to graphs to show the standard deviation.
This shows the spread around the mean.

26. Confidence Interval (CI)
95% certain the mean will be found within the interval
4/5/2017

27. T-test
A common form of data analysis is to compare two sets of data to see if they are the same or different
Null hypothesis: there is NO significant difference between

28. T-test Calculate a value for “t”
Compare value to a critical value (0.05 column)
If “t” is equal to or higher than the critical value we can reject the null hypothesis.

29. Correlation
Correlation is a measure of the association between two factors. The strength of the association between two factors can be measured.
An association in which all the values closely follow the trend is described as being a strong correlation.
An association in which there is much variation, with many values being far from the trend, is described as being a weak correlation.
A value can be given to the strength of the correlation, r.
r = +1 a complete positive correlation
r = 0 no correlation
r = -1 a complete negative correlation
4/5/2017

30. Correlation

31. Correlation

32. Is there a correlation between sunlight intensity and temperature?
4/5/2017