In this article, we’ll describe “Type 1 error”, understanding it, what is false positive, multiple hypotheses testing, consequences of type 1 error and how to avoid type 1 error.
TYPE 1 ERROR:
In statistics and research, hypothesis plays an important and in hypothesis testing, a type 1 error is known as the rejection of a true null hypothesis which is also known as a “false positive” conclusion. On the other hand, type II error is known as the non-rejection of a false null hypothesis which is also known as a “false negative” conclusion.
During a hypothesis testing, when a null hypothesis is rejected even though it is accurate and shouldn’t be rejected, that fault is called a type 1 error. A null hypothesis is established before the onset of a test in hypothesis testing. In some cases, the null hypothesis is that there’s no cause and effect relationship between the tested item and the stimuli being applied to the subject in order to trigger an outcome.
There are possibilities of errors to occur and the null hypothesis is rejected which means it is determined that there is a cause and effect relationship between the variables when in fact it’s a false positive and these false positives are called type 1 errors.
UNDERSTANDING A TYPE 1 ERROR:
Hypothesis testing is a testing process in which testing is done using the sample data and the tests are designed to provide evidence that the hypothesis is supported by the data being tested. In this, a null hypothesis is a belief that there is no statistical significance or effect between the two sets of variables being considered in the hypothesis. And in this, the researcher tries to disprove the null hypothesis.
For example, let’s say the relationship between intelligence and emotion-regulation in adolescents has been tried to find out and the hypothesis selected was that ‘there is a significant relationship between intelligence and emotion-regulation. The researcher would take samples of data and test the intelligence and emotion regulation and finds the correlation between the two. If the test results show that there is a significant relationship between the two then the null hypothesis would be rejected.
This condition is represented as “n=0.” If the results indicate that the stimuli applied to the test subject cause a reaction, then the null hypothesis which states that the stimuli do not affect the test subject would be rejected. A null hypothesis should never be rejected if it’s found to be true, and it should always be rejected if it’s found to be false.
FALSE POSITIVE- TYPE 1 ERROR:
Rejecting the null hypothesis, that there is no relationship between the test subject, the stimuli, and the outcome sometimes can be incorrect. If something other than the stimuli is the cause of the outcome of the test then it can cause a “false positive” result, where it seems that the stimuli acted upon the subject, but the outcome was caused by chance. This “false positive”, leads to an incorrect rejection of the null hypothesis, is then called a type 1 error. In short, a type 1 error rejects a hypothesis that should not have been rejected.
Example of Type 1 error:
- A student has been accused of cheating, here the null hypothesis is that the student is innocent, and the alternative is that he is guilty. A type 1 error, in this case, would be that the student is not found innocent when actually he was and was suspended from school.
MULTIPLE HYPOTHESIS TESTING:
In statistics, multiple testing refers to the increase in Type 1 error that occurs when the statistical tests are used repeatedly, for example, while doing multiple comparisons to test null hypotheses which states that the average of several populations is equal to each other. The fact that the experiment has been repeated multiple times, the probability that the outcome appears at least once, increases. For example, if a coin is tossed 10 times and lands 10 times on tail, it will usually be considered evidence that the coin is biased because the probability of observing such a series is very low for a fair coin. Although, in 10,000 tosses, the chances of 10 tails in a row is more likely to be seen as a random fluctuation in the long series of tosses.
CONSEQUENCES OF A TYPE 1 ERROR:
Type 1 error can sometimes happen because of the bad luck or because the test was not conducted properly with appropriate time duration or the sample size was compromised because of the convenience.
Consequently, a type 1 error brings in a false positive. This means that one will wrongfully assume that his hypothesis testing has worked even though it hasn’t. If it is seen in real-life situations, then this means losing possible sales due to a faulty assumption caused by the test.
HOW TO AVOID TYPE 1 ERRORS:
One can avoid type 1 error by increasing the required significance level before reaching a decision (95% or 99%) and takes more time and a little longer to collect more data. Although statistics never tell us with 100% certainty, it can only provide probability and not certainty. Statistically speaking, even if there is always be a chance of making a type 1 error, one will still be right most of the time if the high enough confidence interval has been set.
In this blog, we’ve described “Type 1 error”, understanding it, what is false positive, multiple hypotheses testing, consequences of type 1 error and how to avoid type 1 error. Please feel free to leave a comment or a suggestion, we appreciate your time.
What causes a Type 1 error?
A type 1 error occurs when a null hypothesis is rejected when it was not supposed to, ie. a true null hypothesis has been rejected.
What is a Type 1 error example?
A type 1 error example would be- A student has been accused of cheating, here the null hypothesis is that the student is innocent, and the alternative is that he is guilty. A type 1 error, in this case, would be that the student is not found innocent when actually he was and was suspended from school.
Which is worse type 1 or 2 error?
Type 1 errors are considered more serious and worse than type 2 errors.
How do you fix a Type 1 error?
One can avoid or fix type 1 error by increasing the required significance level before reaching a decision (95% or 99%) and takes more time and a little longer to collect more data. Although statistics never tell us with 100% certainty, it can only provide probability and not certainty
How do you know if its Type 1 or Type 2 error?
Type 1 error is when a true null hypothesis is rejected whereas type 2 error is when a false null hypothesis and has been failed to reject.