Type 1 Error: How to Reduce Errors in Hypothesis Testing

Type 1 errors (or type I errors) return false positive results for alternative hypotheses, leading researchers to disregard and reject true null hypotheses. In other words, they might cause you to incorrectly believe your statistical experiment was a success. According to statisticians, type 1 errors happen at the alpha level (or statistical significance level) of your results.

A type 2 error (or type II error) means you’ve accepted a false null hypothesis and disregarded your alternative hypothesis. Keep in mind a type 2 error might not mean you have a true positive when it comes to your alternative, just that you’ve returned a false negative for the null. You most likely thought your alternative hypothesis did not return a statistically significant result when it actually did, at least to the point where you can question the null hypothesis.

A type 1 error is a false positive, while a type 2 error is a false negative. In both situations, the researcher arrives at an incorrect conclusion. In a type 1 error situation, the researcher rejects an actually true null hypothesis. In a type 2 situation, the researcher accepts an actually false null hypothesis.

A null hypothesis is a statistical theory that there is no statistically significant relationship or difference when you look at the same variable between two discrete data sets.

As the number of false conclusions about statistics rise, data skews further and further into unreality. A high type 1 error rate in your studies leads people to make wrong conclusions about hypotheses that might influence later research. Vice versa, the same is true with a high type 2 error rate.

Many of the things you can do to mitigate the likelihood of one type of error increases the risk of the other; however, increasing sample size calculations mitigates the likelihood of either occurring.

This type of error can ruin the validity and credibility of statistical tests, but it can also have an impact on real life beyond the lab or academy.

As an example of a type 1 error with a real world impact, suppose you have a null hypothesis stating a specific drug does not effectively treat heart disease and an alternative hypothesis where you state that it does. If you state the drug can treat heart disease, you run the risk of people taking the drug while, in actuality, leaving their heart condition untreated.

In other examples of type 1 errors, you might give rise to a false alarm about a certain hypothesis—for example, perhaps you run a study wherein you wrongly conclude kale causes cancer—when there’s no actual reason for concern.

Reducing your type 1 error rate is an important aspect of statistical analysis and calculation. Keep these tips in mind as you strive to prevent this type of error from occurring:

• Check for influential extenuating factors. False positive and negative results can both arise from ignoring or missing additional factors influencing your data. Think about whether something besides your independent variable might influence possible outcomes. You’re less likely to cause a type 1 error if you can root out any issues like this early on in your research.
• Ensure your data is accurate. Make sure all the data you use is as ironclad and accurate as possible. If you use bad inputs, you’ll get bad outputs. The statistical power of a hypothesis test revolves around ensuring the information you gather is reliable. Make sure your confidence level is as high as possible for every individual element that goes into your research.
• Give yourself a high burden of proof. You’re far less likely to come to incorrect conclusions if you set high standards for yourself. To overturn a null hypothesis, you should be able to point to a large and statistically significant difference between the result of the test you ran and current established research. Use t-tests and other well-received metrics to verify your data.
• Increase random sample size. If you use a larger sample, you help mitigate your risk of causing a Type 1 error. The more information you use to fill out the parameters of your test, the more confidence you will have you represented as thorough a breadth of data as possible. This also has the benefit of decreasing the probability of a type 2 error.
• Set a lower significance level. In general, the level of significance for a test of this ilk is around 5 percent or .05. When your p-value (the results of your statistical analysis) are lower than your significance level, you’re within your rights to reject a null hypothesis in favor of your alternative. Still, this can sometimes lead to false positives. The lower your level of significance, the greater the burden of proof necessary to prove your findings are what they appear to be.

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