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Testing of hypothesis is widespread not only in statistics, but also in the natural and social sciences. When we do a hypothesis test, two types of errors are possible these errors are termed as experimental errors. They are TypeI or TypeII errors. It is important to understand both these effects in order to manage error and report it, for the right interpretation of conclusion from a experiment.
Decision made

Null Hypothesis is actually


True

False


Reject Null

Type I error

Correct rejection

Fail to reject Null

Correct
decision

Type
II error

Type I errors occurs when we reject a true null hypothesis 
The Type I error occurs when a the null hypothesis (H0) is rejected in favor of the research hypothesis (H1), when in reality the 'null' is correct. Type I errors are equivalent to false positives. For example, suppose there is a test that is used to detect a disease in a person. If a Type I error occurs in the test, it means that the test will say the person is suffering from that disease even though he is healthy. Type I errors can be controlled. The value of alpha, which is related to the level of significance that we selected has a direct bearing on type I errors. Alpha is the maximum probability that we have a type I error. For a 95% confidence level, the value of alpha is 0.05. This means that there is a 5% probability that we will reject a true null hypothesis.
Type II errors happen when we fail to reject a false null hypothesis 
Type II error occurs when we do not reject a null hypothesis that is false. It is equivalent to false negatives. From the above said example of a medical test for a disease, if a TypeII error occurs, then it means that the test will not detect the disease in the person even though he is actually suffering from it.The probability of a type II error is given by the Greek letter beta. This number is related to the power or sensitivity of the hypothesis test, denoted by 1 – beta.
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