HBS108 (Topic 8.4 Statistical inference and hypothesis testing: Conf) Mind Map on Null hypoth, chi-squares, p-values, CI, created by shirley.ha on 09/15/2013.
1.1 example of a cohort study looking at the link
between smoking and lung cancer
1.2 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).
1.3 Our cohort study is designed to REFUTE this
Null Hypothesis and if we could do this,
1.3.1 it would mean we could accept the Alternative Hypothesis
1.3.1.1 that there IS an actual difference
between the exposed group and the
non-exposed group
1.3.1.1.1 Before we could make this claim
however, we must apply a test of
significance.
1.4 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
2.1 chi-squares
2.1.1 statistical test used to determine whether two or
more sets of data or populations differ significantly
from one another.
2.1.1.1 based on the comparison of observed
(sample) data
2.1.1.1.1 see if that data significantly differs from the population from
which it was drawn.
2.1.1.1.1.1 use this test for analyzing categorical data.
2.1.2 commonly used statistical technique in
medical research, arising when data
are categorized into mutually exclusive
groups
2.2 p-values
2.3 CI( Confidence Intervals)
2.3.1 concept used for statistical inferences using data
from a sample or samples
2.3.1.1 used to create reasonable bounds for the
population mean or proportion, based on
information from the sample
3 CI
3.1 computed from the sample
data
3.2 has a given probability “that the unknown
(true) population parameter (e.g., the mean
or proportion), is contained within that
interval”
3.3 usually reported as 95% CI
3.3.1 which is the range of values within which we can be 95%
sure that the true value for the whole population lies.
3.4 However 90% and 99% CI can also be used
3.5 the interpretation of the confidence interval also
assists in establishing statistical significance.
3.6 based upon calculated standard errors
3.6.1 give the range of likely values for a population estimate,
based on the observed values from a sample
3.7 Standard errors
3.7.1 can represent the "average"
deviation between actual and
predicted observations
4 looking at standard error of the mean is that
4.1 “provides a statement of probability about the
difference between the mean of the sample and
the mean of the population”