**Commonly used tests of significance are**

For qualitative data: (comparing two proportions)

- Standard error of difference between two proportions
- Chi – square test

For quantitative data: (comparing two means)

- Unpaired – t test (the t – test)
- ‘Z’ – test
- Paired t – test

**Chi – square test**

• Applied on qualitative data

• Equally applicable on small and large samples

• Can be applied if there are more than 2 categories

**Std. error of diff between proportions test**

• Applied on qualitative data

• Applicable on larger samples only (≥30 participants)

• Can compare the proportions between two groups only

**Unpaired or student’s t – test**

• Calculate the observed difference between the two means

• Calculate the standard error of difference between the means of the two groups

• Calculate the t – value as:

t= (observed difference between the two means)/(standard error of difference between the means)

• Determine the degrees of freedom as:

D.f. = n1 + n2 -2

Where n1= number of subjects in the first group

And n2 = number of subjects in the second group

Refer to t – distribution table

• Look against the calculated d.f. and observe the t value under p=0.05

• If the calculated value exceeds this value of t, the p is

**The Z – test**

• Applicable only in larger samples

• Z is calculated instead of t

And:

If Z=1.56, p value is 0.10

If Z=1.96, p value is 0.05

If Z=2.3, p value is 0.02

If Z=2.58, p value is 0.01

Higher the value of Z, lower is the p – value

**Paired t – test:**

Used when:

• Single sample but each subject gives two values (paired values) of the same variable

• Quantitative data

• Random sample

Examples of where paired t – test is applicable:

Value before and after treatment e.g. systolic BP of each subject before taking treatment and the systolic BP of the same subject after treatment

Comparing effects of two different drugs on the same individual for example blood sugar level after 2 hours of drug A and the same after drug B

Checking the accuracy of a new measuring instrument against a standard one, for example Hb estimation by an new method against the same by colorimeter**For more details on the tests** tests of significance**References:**

Tiwari P. Epidemiology Made Easy. New Delhi: Jaypee Brothers; 2003

Gordis, L. (2014). Epidemiology (Fifth edition.). Philadelphia, PA: Elsevier Saunders.

Bonita, R., Beaglehole, R., Kjellström, T., & World Health Organization. (2006). Basic epidemiology. Geneva: World Health Organization.

Schneider, Dona, Lilienfeld, David E (Eds.), 4th ed. Lilienfeld’s Foundations of Epidemiology. New York: Oxford University Press; 2015

K. Park. Principles of Epidemiology and Epidemiologic Methods. In Park’s Textbook of Preventive and Social Medicine. 24th Ed. Jabalpur: Banarasidas Bhanot, 2017: pg 58 – 145

**Steps in Investigation of an Epidemic:** http://www.ihatepsm.com/blog/epidemiology-steps-investigation-epidemic**Tests of Significance**: http://www.ihatepsm.com/blog/epidemiology-tests-significance**Monitoring and Evaluation**: http://www.ihatepsm.com/blog/epidemiology-monitoring-and-evaluation**Advantages and Disadvantages of Case Control Studies**: http://www.ihatepsm.com/blog/epidemiology-advantages-and-disadvantages-case-control-studies**Advantages and disadvantages of cohort study**: http://www.ihatepsm.com/blog/epidemiology-advantages-and-disadvantages-cohort-study**Basic Concepts in Epidemiology:** http://www.ihatepsm.com/blog/epidemiology-basic-concepts**Types of Epidemiological Studies:** http://www.ihatepsm.com/blog/epidemiology-types-epidemiological-studies**Differences between Case – control and cohort study**: http://www.ihatepsm.com/blog/epidemiology-differences-between-case-%E2%80%93-control-and-cohort-study**Uses of epidemiology:** http://www.ihatepsm.com/blog/epidemiology-uses-epidemiology**Blinding in Experimental Studies:** http://www.ihatepsm.com/blog/blinding-experimental-studies**Evaluation of a Screening Test**: http://www.ihatepsm.com/blog/epidemiology-evaluation-screening-test