1. STATISTICAL TESTS FOR SCIENCE FAIR PROJECTS

2. Null vs. alternate hypothesis
H0: Tomato plants do not exhibit a higher rate of growth when planted in compost rather than soil. HA: Tomato will exhibit a higher rate of growth when planted in compost rather than soil.

3. The Null Hypothesis
The null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data to contradict it.

5. P-value
A 95% level of confidence means we reject the null hypothesis if p falls outside 95% of the area of the normal curve.

6. P-value
A P-value of less than 0.05 is considered a significant difference. A P value of 0.04 would mean that 4% of the time or less, we would observe this difference between the control and experimental groups due to chance alone. However a p value of 0.10 would mean 10% of the time this difference could be due to chance and not the independent variable in your experiment. The difference can not be considered significant.

7. Your project data will most likely fit into one of these categories:
Student T-test
T test for 2 groups
1 way ANOVA
2 way ANOVA
Correlation; Regression
Chi square

8. Student T-test
Only 1 group, for example a group of students take a pre-test and then after learning the material take a post test.
Data would need to be continuous for DV

9. T-test for 2 groups 2 named groups are compared
Continous data for DV (data must be #’s)
Nominal IV (i.e. men; women)

10. 1 WAY ANOVA 3 or more named groups compared.
Nominal IV variable (i.e. old, middle-aged, young)
DV is continuous

11. Correlation & Regression
IV is continous
DV is continous
Line graph can be produced
Ex. Athletes of certain weight or height, etc. can jump higher or lift more

12. Chi-square test Looking at observed vs. expected Sex ratios
Punnett squares
1 IV and 1 DV
DV & IV are nominal