Mortality Analysis

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

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

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

Significance- 0.05 df = 1 Critical value is?

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.

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

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