Significance Testing:
Significance tests are employed to make decisions on the basis of small information available from the samples. With all such decisions an amount of risk is involved.
The value of the significant difference depends upon:
- The greater the standard deviation, for a difference (between the means) to be significant, it (difference) should be more.
- The larger the sample, for a difference to be significant, it (difference) should be smaller.
- Level of variation indicates the level (0.1%, 5%) at which the difference is significant.
Three variations under significance testing can be considered:
- Testing two random samples as regards their sample means.
- Testing a sample mean against a lot mean.
- Testing sample means of two samples, one being manufactured by the existing process and the other by the modified process.
Four different cases can be analysed:
- Testing the difference between sample mean and lot mean employing bigger sample size (say containing more than 25 pieces).
- Testing the difference between sample mean and lot, population, or true mean, employing small sample size (say containing less than 25 pieces).
- Testing the difference between sample mean and the mean of another sample from another lot employing large sample size.
- Testing the difference between sample mean and the mean of another sample from another lot employing small sample size.
