How do you analyze the data?
S.ChS.1 Science Inquiry: Questions and Design - The learner will evaluate the importance of curiosity, honesty, openness, and skepticism in science.
1. Due - Discussion Section
2. March Mentor Check - 3/30 - A and 3/29 - B
Download ASR Research Goals Sheet
3. Semester Timeline - Due Dates
c.3/28 & 3/29 - 1st Draft of Final Paper
- 100 total Data Points
- Data Tables & Graphs
- Discussion - analysis of data and graphs
- Statistical Tests - what test?, run the test
d. 4/10 through 4/17 - Poster and PowerPoint Work Week
e. 4/12 & 4/13 - Revised Final Paper
f. 4/18 & 4/19 - 1st Draft of Poster and PowerPoint
g. 4/26 & 4/27 - April Mentor Check
h. 5/2 & 5/3 - Final Presentations Begin
- Absent on Your Presentation Day? - Your presentation will be after school
- You will provide 5 ASR students to watch and evaluate your presentation
4. Literature Review- make modifications on Literature Review
Download Final Paper Criteria and Order of Paper
7. t-Test, ANOVA, and Chi-Square
a. t-Test - for quantitative data; can be used to determine if observed differences between means of two groups are statistically significant.
b. ANOVA - for quantitative data; used instead of the t-Test when you are comparing three or more groups.
c. Chi-Square - for qualitative data; can be used to determine if differences between frequency distributions are statistically significant.
8. Statistical Analysis - below are two websites to analyze your project data
9. Data Analysis - Chapter 11 - Statistical Techniques for Analyzing Quantitative Data
Extremely significant? |  |  |  |  |  |
Once you have set a threshold significance level (usually 0.05), every result leads to a conclusion of either "statistically significant" or not "statistically significant". Some statisticians feel very strongly that the only acceptable conclusion is significant or 'not significant', and oppose use of adjectives or asterisks to describe values levels of statistical significance.
Many scientists are not so rigid, and so prefer to use adjectives such as “very significant” or “extremely significant”. Prism uses this approach as shown in the table. These definitions are not entirely standard. If you report the results in this way, you should define the symbols in your figure legend.
Here is the scheme that Prism uses:
P value | Wording | Summary |
< 0.0001 | Extremely significant | **** |
0.0001 to 0.001 | Extremely significant | *** |
0.001 to 0.01 | Very significant | ** |
0.01 to 0.05 | Significant | * |
≥ 0.05 | Not significant | ns |
Prism stores the P values in double precision (about 12 digits of precision), and uses that value (not the value you see displayed) when it decides how many asterisks to show. So if the P value equals 0.05000001, Prism will display "0.0500" and label that comparison as "ns".