The document discusses type I and type II errors in sampling, explaining that a type I error is a false positive while a type II error is a false negative. It provides the example of COVID-19 testing to illustrate these errors, where a type I error indicates having the virus when one does not, and a type II error indicates not having the virus when one does. The document also suggests methods to reduce these errors, such as adjusting the significance level for type I errors and increasing sample size for type II errors.

2. BASIC TERMS

13. NON-PROBABILITY
SAMPLING

18. SNOWBALL SAMPLING

19. Tapan Kumar Mahato
Assistant Professor
B.Pharmacy College Rampura, Godhra, Gujarat
tapan.mahato@gmail.com

20. Type I and Type II
errors in sampling

21. Type I error is a false positive
conclusion
Type II error is a false negative
conclusion.

22. Example:
You decided to get tested for COVID-19
based on mild symptoms.
There are two errors that can occur:
Type I error (false positive): the test
result says you have coronavirus, but
you actually don’t.
Type II error (false negative): the test
result says you don’t have coronavirus,
but you actually do.

23. How to reduce the risk of making a
Type I error?
The significance level is set usually at
5% or 0.05.
This means that our results will have a
chance of only 5% if the null
hypothesis is actually true
Using this method type I error can be
minimized

24. How to reduce the risk of making a
Type II error?
To reduce the risk of a Type II
error,
We can increase the sample
size or the significance level

25. THANK YOU