Compares means & standard deviation of 2 data sets
If more than 30 samples per data set then infinity degrees of freedom
If less than 30 samples per data set then use n1 + n2 - 2 (n = number of samples in data set)
Null hypothesis is "No significant difference between sets of data"
If t > critical value then reject the null hypthesis, there is a signifcant difference
Standard Deviation:
Square root of variance
Variance: work out mean, subtract mean from each result and square the difference, average all square differences
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Spearman Rank Correlation
Looks for correlation or relationship
Null hypothesis is "No significant correlation between variables x and y"
If calculated value (rs) is bigger or equal to the critical value then reject null hypothesis as there is a significant correlation between the variables
Positive rs value indicates positive correlation, negative value indicates negative correlation
Values of rs are between -1 and 1
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Chi-Squared Test
Used to compare observed and expected values
Can only be used on categorical data e.g. genetic ratios
Null hypothesis is "There is no significant difference between the observed and the expected values"
The number of degrees of freedom is found by using the number of categories minus one
If the calculated value of chi squared is greater than or equal to the critical value then reject the null hypothesis as there is a significant difference between the observed and expected
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Lincoln Index
Used to estimate population numbers:
Number marked in 2nd sample ÷ total caught in 2nd sample =
Number marked in 1st sample ÷ size of whole population (n)
Assume:
No immigration
No emigrations
No births/deaths
Random mixing
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Simpson's Diversity Index
Measures species richness
High value of D indicates stable and species rich environment
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