Null hypoth, chi-squares, p-values, CI

Null hypoth, chi-squares, p-values, CI

1. E.g of null hypothesis

   • example of a cohort study looking at the link between smoking and lung cancer
   • There is no difference in terms of rates of lung cancer between the group that smoked (the exposed group) and those that did not smoke (the non-exposed group).
   • Our cohort study is designed to REFUTE this Null Hypothesis and if we could do this,
     • it would mean we could accept the Alternative Hypothesis
       • that there IS an actual difference between the exposed group and the non-exposed group
         • Before we could make this claim however, we must apply a test of significance.
   • Applying the significance test is a crucial step in hypothesis testing that is the final step in the quantitative research process

2. 3 tests of sign aka measures of precision

   • chi-squares
     • statistical test used to determine whether two or more sets of data or populations differ significantly from one another.
       • based on the comparison of observed (sample) data
         • see if that data significantly differs from the population from which it was drawn.
           • use this test for analyzing categorical data.
     • commonly used statistical technique in medical research, arising when data are categorized into mutually exclusive groups
   • p-values
   • CI( Confidence Intervals)
     • concept used for statistical inferences using data from a sample or samples
       • used to create reasonable bounds for the population mean or proportion, based on information from the sample

3. CI

   • computed from the sample data
   • has a given probability “that the unknown (true) population parameter (e.g., the mean or proportion), is contained within that interval”
   • usually reported as 95% CI
     • which is the range of values within which we can be 95% sure that the true value for the whole population lies.
   • However 90% and 99% CI can also be used
   • the interpretation of the confidence interval also assists in establishing statistical significance.
   • based upon calculated standard errors
     • give the range of likely values for a population estimate, based on the observed values from a sample
   • Standard errors
     • can represent the "average" deviation between actual and predicted observations

4. looking at standard error of the mean is that

   • “provides a statement of probability about the difference between the mean of the sample and the mean of the population”