While doing the hypothesis test, you will come across with two types of errors: type I and type II. Risks are inversely related in these two types of errors and can be analyzed by their level of significance. So, it is necessary to understand that which of the errors will come up with severe consequences depending on situation.

The hypothesis test will give you 100% certainty. The tests are completely dependent on probabilities, so you have the chance of extracting an incorrect conclusion.

**1. Type I error**

In case null hypothesis turns out to be turn and still you reject it, then it comes out to be type I error. The probability of error is Î± which is known to be a level of significance that you decide in hypothesis test. In case of Î± 0.05 it directly indicates that you are capable for accepting 5% chances of being wrong. If you are conscious about reducing the risk it is necessary to adopt lower value of Î±.

**2. Type II error**

In case the null hypothesis turns out to be false and still you do not reject it. This is when you make type II error. Probability for such error is Î² which would depend on power test. There is the possibility for decreasing the risk of performing such error by ensuring that the test contains enough power. It is only possible when you ensure that the sample size is large enough and can easily detect the difference.

**Avoid doing errors**

Both the errors are known to be a part of hypothesis testing. How would you explain the concept of p-value to a five-year-old? This is an important question that is often asked. Though, there is no such way through which you can completely eliminate errors, but there is a possibility of minimizing them. You can decrease a value of Î± from 0.05-0.01 which would signify 99% level of confidence. When you try to decrease the probability of type I error, other type of error will automatically increase.