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Chi Square Test. Gitanjali Batmanabane. At the end of this session you will be able to:. Prepare a contingency table Realise which study designs are suitable for applying the chi square test Understand the assumptions / limitations of the chi square test. Know thyself.

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Presentation Transcript
chi square test

Chi Square Test

Gitanjali Batmanabane

at the end of this session you will be able to
At the end of this session you will be able to:
  • Prepare a contingency table
  • Realise which study designs are suitable for applying the chi square test
  • Understand the assumptions / limitations of the chi square test.
slide3

Know thyself

Why does he keep saying this all the time?

slide4

No, my son, but I understand something about this “NOT KNOWING”

Excuse me sir, you say “know yourself” all the time but do

YOU KNOW YOURSELF?

what is it
What is it?
  • Test of proportions
  • Non parametric test
  • Dichotomous variables are used
  • Tests the association between two factors

e.g. treatment and disease

gender and mortality

associations and causal associations
Associations and Causal Associations

Relationship

between variables

Not statistically

associated

Statistically

associated

Non-causal

Causal

Indirectly

causal

Directly

causal

slide7

Contingency (2X2) table

  • Enter number of subjects – not percentages, ratios, averages etc.,
  • Each subject can be entered only once
slide8

Out of 25 women who had uterine cancer, 20 claimed to have used estrogens. Out of 30 women without uterine cancer 5 claimed to have used estrogens.

Total

Total

slide9

Out of 25 women who had uterine cancer, 20 claimed to have used estrogens. Out of 30 women without uterine cancer 5 claimed to have used estrogens.

Total

25

30

25

30

55

Total

assumptions limitations
Assumptions / Limitations
  • Data is from a random sample.
  • A sufficiently large sample size is required (at least 20)
  • Actual count data (not percentages)
  • Adequate cell sizes should be present. (>5 in all cells- if less number present apply Yates correction)
  • Observations must be independent.
  • Does not prove causality.
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