1. Online Templates for Basic Statistics: Rubric Lines 5 & 6 & (4)
Cindy Alonso
David Buncher
AP Research

2. Common statistics: Methods, Results, Discussion
Number of participants
Mean
Standard deviation
t-tests
ANOVA
Chi Square
Regression and R2

3. Results Presents the findings, evidence, results, or products
Tables, data tables, charts, graphs etc…
Label all tables and refer to tables and graphs in the text
Note the p values, t-values, F-values

4. Discussion/Conclusion
Interprets the significance of the p values of the results or findings
Explores connections to the original research question
Include more lit review
Discuss the implications and limitations of the research
Line 5: establish argument from results
Line 6: Data analysis from results
Limitations, future research

5. Variables Independent variable- manipulated variable
How much water is added to a pea plant
Dependent variable- the outcome
How tall the flower grows
Control- the pea plant without receiving any water
Constants- same temperature, light, etc
Usually mentioned in “Methods”

6. Number of participants
Number of groups
Number of participants in each group
How were the participants selected
Filtering data: males/females, AP/non AP, …
“Methods”

7. Results: Mean and standard deviation
# of participants (N), the “more” the better (discuss)
Mean (average) Add up all the numbers and divided by the # of responses
Standard deviation- how spread out is the data?
Range
Makes nice looking graphs
and charts for results

8. t-tests Are the two means statistically (significantly) different?
2 independent means:
Dominos vs Papa Johns delivery time
2 dependent means:
Pretest vs posttest

9. t-tests Null hypothesis: No difference between the two means
P level usually < .05: results and conclusions
95% confident of your results
1-tailed or 2-tailed outcome
Bar graphs with p values in results or conclusions sections

10. ANOVA
Are the “greater than two” means statistically (significantly) different from each other?
F statistic, p value
Bar graphs in results or conclusion

11. Chi Square
Let’s say you want to know if there is a difference in the proportion of men and women who are left handed and let’s say in your sample 10% of men and 5% of women were left-handed.
For example, you ask 120 men and 140 women which hand they use and get this:
Left-handed
Right-handed
Men 12
108
Women 7
133

12. Interpretation
Greater differences between expected and actual data produce a larger Chi-square value. The larger the Chi-square value, the greater the probability that there really is a significant difference.
Tables in results
Discussion of p value in discussion section

13. Correlation (Linear regression)
Relationship between one independent variable and one dependent variable:
Y = mx +b straight line
Prediction model
Y = dependent variable
x = independent variable
b = dependent variable when independent variable = 0 (y-intercept)
m= slope !!!
Discussion section

14. Scatter plot to determine Correlation
Linear line of best fit y=mx+b

15. Correlation Caution: cause and effect Obvious relationships: colinear
R strength of correlation R = 1 is perfect
P value

16. R2 R-squared (R2) is always between 0 and 100%:
0% indicates that the model explains none of the variability of the response data around its mean.
100% indicates that the model explains all the variability of the response data around its mean.
In general, the higher the R-squared, the better the model fits your data.

17. Good Luck
David Buncher
Cell: