If you are a statistics student, you must know how to calculate statistical power. Often students fail to find out the efficient way to calculate the power in statistics.
Although the power calculation concept is not so important for the students, they should know the basics of this concept.
If you are one of those who are struggling to find out the proper way to find out the statistical power, then you no need to worry about that.
In this blog, we will guide you on how to calculate power statistics and other factors to consider when you are going to calculate power in statistics.
But before that, we should know what power in statistics is; let’s know it.
In some cases, power is also known as sensitivity. It is the probability of having the effect of a study from a chance. In order to clear your concept regarding the power, you should be clear about the type 1 and type 2 errors. The correct definition of power varies with these errors.
Type 1 error: In favor of a false alternative hypothesis, the rejection of the null hypothesis is known as type 1 error. Type 1 error is called Alpha.
Type 2 error: In favor of a true alternative hypothesis, failure in rejecting a false null hypothesis is known as type 2 error. This error is called Beta.
You should know
- The power of a hypothesis test lies between 0 to 1.
- A hypothesis test is good at detecting a false null hypothesis- when the power is close to 1.
- The researcher set the value of Beta in a smaller range, commonly at 0.2 or less.
- The power can not go below 0.8 for the research tasks, but it can be higher.
Factors affecting power, when we calculate it
In the way to calculate the power in statistics, below are a few primary factors that affect the calculation.
- Alpha or you can say significance Level
- Size of the sample
- Variance and variability
- The magnitude of the variable’s effect
When the researchers increase the sample size, the power will increase along with the surge in effect size and significance level.
Let’s talk about how to calculate power statistics.
How to calculate power in statistics?
When you are done with a hypothesis test and make a decision regarding this test, it is not certain or 100 % guaranteed that you are right. You should be aware of type 1(Alpha) and type 2(Beta errors).
If the power of a test is larger or high, it denotes the precision degree of the hypothesis. Suppose a study has 90% power, it implies that it has 90% chances of testing significant results.
High statistical power: Test results are better, but chances of type 2 error are high.
Low statistical power: Test results are not upto the mark.
Well, it is difficult to calculate the power manually. We use two types of software to calculate the power.
SAS( Sample Size Analysis Software)
PAS( Power Analysis Software)
Power Analysis Software(PAS)
It is a method to find out the statistical power. When the hypothesis is wrong or not right, the power is possible to dismiss a zero. When you fail to reject a false null hypothesis, the type 2 error affects the power value. So, power can be defined as not having a type 2 error.
For example, you are doing a medicine test, and this medicine is working. You need to do a number of tests with an effective or real medicine and an inactive medicine. If the power of your tests will be 0.8, it refers to 80% chances of your test’s significant outcomes.
However, in 20% of cases, your test reports are not significant. In this way, the power informs you regarding the differences between the two values.
Why do we use the power Analysis method?
We use the analysis method for the various reasons involving-
In order to determine the count of tests that we require to obtain a proper size effect. It tells us how many tests we have to exclude that reject the null hypothesis.
Apart from this, it is used to calculate power. It is best in the case when you do not have a flexible budget(100 samples), and you need to know the effect by testing that number.
Moreover, it is also useful in checking the validity of your research. Power calculations are a complicated concept and possible to get with the help of computer softwares.
You can check your experiment with the other available tests. You can check your reliability regarding your experiment.
How can we calculate sample size?
In order to get sample size, we need to consider
- Identify Hypothesis test
- Identify the significance of the test
- Determine the effect size that is smallest and is of scientific interest.
- Consider the other parameters and their values that are required in calculating power function.
- Determine the desired test power.
In this blog, we have explored the ways on how to calculate power statistics. You need computer software to calculate the power of a hypothesis test. You can determine the power from the above stated two software. I hope this blog will be helpful for you in the context of understanding the calculation of power and the importance of power calculation techniques.