210752
Description
Mind Map by shirley.ha, updated more than 1 year ago
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Created by shirley.ha
about 11 years ago
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Null hypoth, chi-squares, p-values, CI
- 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.
- Before we could make this claim
however, we must apply a test of
significance.
- that there IS an actual difference
between the exposed group and the
non-exposed group
- it would mean we could accept the Alternative Hypothesis
- Applying the significance test is a crucial step in
hypothesis testing that is the final step in the quantitative
research process
- example of a cohort study looking at the link
between smoking and lung cancer
- 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.
- use this test for analyzing categorical data.
- see if that data significantly differs from the population from
which it was drawn.
- based on the comparison of observed
(sample) data
- commonly used statistical technique in
medical research, arising when data
are categorized into mutually exclusive
groups
- statistical test used to determine whether two or
more sets of data or populations differ significantly
from one another.
- 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
- used to create reasonable bounds for the
population mean or proportion, based on
information from the sample
- concept used for statistical inferences using data
from a sample or samples
- chi-squares
- 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.
- 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
- 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
- can represent the "average"
deviation between actual and
predicted observations
- computed from the sample
data
- 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”
- “provides a statement of probability about the
difference between the mean of the sample and
the mean of the population”
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