Knowing **How to calculate power in statistics** is very important for students and statisticians because it helps in detecting the chance of getting results from a study. Thus, you can decide whether you want to pursue that study or not. This blog aims to tell you **how to calculate power in statistics**, what is the significance to know it and guidelines thereon.

**What is Statistical Power?**

Summary

Before knowing **how to calculate power in statistics** we should know the meaning of statistical power. The statistical power of a study shows the actual results of the hypothetical test. It means that it represents the actual effect from one of chance and thus not based on the null hypothesis. It is also called Sensitivity. For example if a study has 90 % statistical power then it means that there is 90% chance of the test to have significant results. Thus, it is very important to know **how to calculate power in statistics**.

**Interpretation of Statistical Power**

- High statistical power shows the high probability of results of the test being true. So if statistical power is high then the tests results are likely accurate and valid.
- Likewise, Low statistical power shows that the test results are likely inaccurate and thus questionable.

**Methods**

There are many methods to calculate the power of Statistics. If we talk about **how to calculate the power of statistics** effectively and efficiently, the Power Analysis method is the best choice. Power Analysis calculates the statistical power of a test while presuming that the actual effect is there. It is basically a probability of rejecting a null hypothesis when it is false. Since type II error occurs when we fail to reject a false null hypothesis thus, we can say that statistical power is the probability of not making type II error.

**Parameters of statistical power**

To know **how to calculate power in statistics** one should know the following parameters of such calculations. They are

- The effect size
- The sample size ( represented as N)
- The alpha significance criterion ( represented as
*a*) - The statistical power

All of these 4 parameters are interrelated mathematically. So we can find any of them with the help of rest parameters.

**Steps**

Following are the steps to be followed to calculate power of statistics –

**1.** **Region of Acceptance**

Firstly we have to compute the region of acceptance for the study. It is done to define a region of acceptance for our study.

**2.** **Critical Parameter Value**

Then we have to specify the critical parameter value for our study. The critical parameter value is basically an alternative value to the value specified in our null hypothesis of the study. Effect size is the difference of critical parameter value and value specified in the null hypothesis. Thus, effect size is the value obtained by subtracting the value specified in the null hypothesis from the critical parameter value.

**3.** **Calculate Power**

We have to assume that the true parameter of population and the critical parameter are equal. On the basis of such assumptions we have to calculate the probability of how much the sample estimate of the population parameter is going to fall outside the region of acceptance and this probability is the power of statistical tests.

**Software to calculate statistical power**

Since it is very difficult to calculate statistical power by hand; so we can use different software to make the process of how to calculate the power of statistics easily. Following are the two soft wares –

**1.** **SAS 2. PASS**

**Steps to compute sample size**

Sample size is the most important parameter to calculate power of statistics therefore it is very important to know how to calculate sample size in order to know **how to calculate power of statistics.**

- First step is to specify hypothesis test
- Then to specify what is the importance of such test
- Specify the smallest effect size of scientific interest
- Determine the values of all other parameters
- Write down the desired power of the test

**Significance**

If we **know how to calculate the power in statistics** then it can help you to decide whether you want to proceed with the statistical study or not. Since the power in statistics tells us the probability of getting a result , thus it is very easy to know the probability of getting the result of the statistical study. For example if you know that the probability of getting results is just 20% then definitely you would not proceed with the study rather you would do the study whose statistical power is high. Accordingly, if you know how to calculate it then you can detect the chance of success rate of your study.

**Conclusion**

We can conclude that power in statistics is very useful for conducting a study as it helps us to decide the probability of success of study. We can compute the likeliness of results of study if we know **how to calculate power in statistics**. Thus it is an efficient tool to conduct study and to decide what hypothetical test you should take. We all think that computing power in statistics is very difficult because we don’t know **how to calculate statistics power** although it is very easy to calculate the same as we have discussed above.

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