1. Mortality Analysis

2. Chi-Squared Test -aka goodness-of-fit test
Compare observed data with data we would expect to obtain according to a specific hypothesis
Yate’s Correction
- used in 2x2 tables and small data sets to prevent overestimation of statistical significance

3. Statistical significance = probability that the observed relationship or a difference in a sample occurred by pure chance
Probability of error involved in accepting our result as valid
p = 0.05
5% chance that the relationship between variables is a fluke
Biological/ecological studies often use 0.05; medical studies will use 0.01
p<0.05 = significant result ; p<0.01 = highly significant result
conclude that some factor other than chance is operating for the deviation

4. Degrees of Freedom (df)
Number of values in the final calculation of a statistic that are free to vary -Mathematical restriction
Example: you have four numbers (a, b, c and d) that must add up to a total of m; you are free to choose the first three numbers at random, but the fourth must be chosen so that it makes the total equal to m
Thus your degree of freedom is three
df= n-1; n being the categories/habitats in our case

5. Significance- 0.05
df = 1
Critical value is?

6. Now for Our Data:
Null Hypothesis: There is no significant difference in shrimp survival between vegetated and non-vegetated zones. Alternate Hypothesis: There is a significant difference in shrimp survival between vegetated and non-vegetated zones.

7. Alive
Dead
Total
Vegetated a c NA
Non-vegetated b d NB NF NS N

8. Excel Sheet