hypothesis testing in statistics

Difference between type 1 and type 2 errors in statistical hypothesis testing: How to interpret it

Difference between type 1 and type 2 errors

In statistical test theory, the concept of a statistical error is an integral part of hypothesis testing. The Hypothesis test is about choosing between the two hypotheses, the Null Hypothesis or Alternative Hypothesis. The Null hypothesis is presumed to be true until the data provide convincing evidence against it.

What is Hypothesis Testing and How to do Hypothesis testing

Any hypothesis testing is not 100% accurate. There will be scope for some error. There are two types of error that can occur. These errors are :

  • Type 1 and
  • Type 2.
Difference between type I and type II errors
Type I and type II errors in statistical hypothesis testing

In the above image we can see there are four different … Continue Reading

What is Hypothesis testing in statistics: How to do Hypothesis testing,5 steps process

What is Hypothesis testing in statistics

Hypothesis testing in statistics is a process by which we are confirming our hypothesis or prediction statistically. Scientists mainly use it to test the specific prediction from some theory or hypothesis or even from gut feelings.

Examples of Hypothesis Testing:

  1. The ability of Vitamin C to cure or prevent cold,
  2. Children of obese parents are more likely to be obese
  3. Men have taller than women
  4. Tenure calling operators have less handling time than a new operator.

Type of Hypothesis testing :

  1. Null Hypothesis : Null hypothesis states that there is no difference in parameters for two or more populations. If any observed difference in a sample is due to chance or sampling-related error, it
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