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Statistical Analysis I have all this data. Now what does it mean?. Continuous quantitative – measurement scale divisible into partial units Ex-Distance in kilometers Discrete quantitative - measurement scale with whole integers only Ex- number of wolves born in given year
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Ex-Distance in kilometers
Discrete quantitative- measurement scale with whole integers only
Ex- number of wolves born in given year
Quantitative data can be subdivided into:
Ratio - with equal divisible intervals & absolute zero
Interval - does not have absolute zero
Qualitative Nominal- objects are named or can’t be ranked
Example- Gender (male/female)
Qualitative Ordinal - objects are placed into categories that can be ranked
Example- activity of an animal on a scale of 1 to 5Is your data Quantitative or Qualitative?
Decide which type of data you have__________________
Central tendency (How different 2 sets of Data is)
Variation (How spread out the data is)
… is not going to do your job for you.
not going to tell you what test to select
not going to tell you if the test you selected is the right one
not going to tell you how to interpret the test results.
Decide which type of data you have, parameters you will need to calculate and on your Excel chart, enter the formula for each of the parameters.
The t-test (or Analysis of Variance):
two or more groups
to compare measurements of each group.
The Chi-square test:
counts that can be placed into yes or no categories, or categories such as quadrants.
The Pearson R Correlation:
to test how the values of one event or object relates to the values of another event or object
…..states that there is no difference between the mean of your control group and the mean of your experimental group. Therefore any observed difference between the two sample means occurred by chance and is not significant.
If you can reject your null hypothesis then there is a significant difference between your control and experimental groups. Hence accept the alternative (original hypothesis).
Write your null hypothesis _____________________________
Probability of error or p-value < 0.05 means that
the error in the research is 5/100 or below 0.05
(95% results have no error)
Level of significance () - It communicates probability of error in rejecting Null hypothesis
p-value < 0.05 means that the probability of error in the research is 5/100 (95% results with no error)
Degree of freedom (df) - It is number of independent observations in a sample.
t-test df = (n1-1) + (n2-1)
Chi-square df = (#rows – 1) (#columns – 1)
Pearson R correlation df = (n-2) subtract 2 from the number of comparisons made.
T test Chi square tables.doc
Find the table value for the t-test and the Chi-square test
(using calculated degrees of freedom and the Level of Significance of 0.05 = 95%)
Compare calculated value to table value.
Calculated value < table value
Null hypothesis is NOT rejected
Calculated value > or = table value
Null hypothesis is rejected.