It is possible for companies and organizations to damage their conversion rates by making changes to their websites based on inaccurate information. An example of hypothesis testing is analyzing a postulation using sample data. It is the purpose of the test to demonstrate that the postulation or hypothesis is supported by the data that has been collected. A null hypothesis states that there are no numerical implications or consequences between the two data sets, variables, or populations being assessed. It is only natural that a tester would strive to negate the null hypothesis. Ideally, a null hypothesis should never be discarded when it is proven to be true, and it should always be discarded when it is proven to be false. Errors can, however, occur under many circumstances.
In this article, we present a brief description of Type 1 errors, their importance, when they occur, the odds of making a Type 1 error, and how to handle those errors.
When Does Type 1 Error Occur? A Complete Overview
What are Type 1 Errors?
A type 1 error is the sort of error that happens during the hypothesis testing procedure when null hypotheses are discarded, despite the fact that they are accurate and should not be discarded. A Type 1 error (or type I error) is a term that is commonly used in statistics to describe one kind of error that is generated in testing when a decisive (or conclusive) answer is acknowledged even though the test is actually indecisive (or inconclusive). Now, if we speak scientifically, a type 1 error is cited as the denial of a true null hypothesis, as a null hypothesis is described as a hypothesis where there is no significant disparity between mentioned populations, and differences (if any) that have been observed are due to sampling error or experimental mistake.
In the process of hypothesis testing, a null hypothesis is created before the beginning of the test. In some circumstances, the null hypothesis presumes that there is no cause and consequence affiliation between the item that is being tested and the stimuli that are being implemented for the test in order to prompt a conclusion to the test.
However, errors can arise through which the null hypothesis has been discarded, which means it is assumed that there is a cause and consequence affiliation between the two testing variables when, in certainty, it is proved to be a false positive. These false positives are also termed type 1 errors.
It is a false belief that a disparity while conducting a test has made a statistically significant change.
Why Type 1 Errors are important?
Type 1 errors play a huge role in your conversions. For example, if you perform an A/B test on two different versions of a page and mistakenly conclude that the second version is better than the first one, then you could possibly notice a huge impact on conversions. For example, if you conduct an A/B test on two different versions of the same page and wrongly conclude that version B is the winner, you could see a huge fall in conversion rates when you get that modification live for all your viewers to see. As said in the above statement, this could be the consequence of poor testing techniques, but it might also be the outcome of a random possibility. Type 1 errors may (and perhaps they do) result from faultless testing.
When you make any modifications to a website on the basis of A/B testing, then it is essential for you to appreciate that you may be functioning with incorrect conclusions that have been generated by type 1 errors.
If you understand a type 1 error correctly, then it allows you to:
- Select the level of risk that you’re keen to allow (for example, enhance the sample size to attain a higher level of statistical significance).
- Conduct appropriate testing to minimize the possibility of human-caused type 1 errors.
- Be attentive to identify when a type 1 error may have resulted in a fall in conversion rates so you can fix them in good time.
It is almost impossible to achieve a 100% statistical significance (and it’s generally impractical to seek a 99% statistical significance, as it necessitates an excessively large sample size in comparison to a 95%–97% statistical significance). The aim of Conversion Rate Optimization (CRO) is not to get it accurate all the time; rather, its duty is to make the right call at the right time the majority of the time. And when you comprehend the mechanism of type 1 errors, you enhance your possibilities (or odds) of getting it correct.
When does a Type 1 Error occur?
A Type 1 error occurs when a false-positive outcome arises because a tester has discarded a null hypothesis that, in reality, was true in the population.
A Type 1 error can arise from two causes: random chance and improper research techniques.
Random Chance: Be it a pre-election poll or an A/B test, it is impossible for any random sample to flawlessly reflect the population that it aims to depict. Since all researchers only consider a small fraction of the total population as a sample, it is quite possible that the generated outcome doesn’t predict correctly.
Statistical significance measures the possibility that the outcome of an A/B test was generated by random chance. For example, you run an A/B test that demonstrates that the second version of the same sample is outperforming the first version with a statistical significance of 90%. This means there is a 10% possibility that these outcomes were generated by random chance. However, you can raise the level of statistical significance by increasing the sample size, but that would require more traffic and therefore take more time than usual. Lastly, it is your job to maintain an equilibrium between your preferred level of correctness and the resources that have been made available.
Improper Research Techniques: When you perform an A/B test, it is equally important for you to collect sufficient data to attain your desired level of statistical significance. Amateur researchers might just start conducting the test and pull the plug as soon as they realize there is a “clear winner” – way before they’ve accumulated sufficient data to reach the goal.
What are the odds of making a Type 1 Error?
A Type 1 error is the process of discarding a null hypothesis when it is actually true. The probability of making a type 1 error is determined by the significance level (alpha or). It is the value that you set at the start of your study to evaluate the statistical probability of acquiring your results (which is also known as the “p value”).
The significance level is generally set at 0.05 or 5%, which means that the p-value only has a 5% possibility of occurring, or fewer, if the null hypothesis is true, in reality.
If the p-value of your test is less than the level of significance, it indicates that your results are statistically compelling and reliable to the other hypothesis. And if your p-value is greater than the level of significance, then it indicates that your results are statistically insignificant.
Type 1 Error Rate
Below we have presented a null hypothesis distribution curve which depicts the odds of acquiring all feasible results if the tests were repeated with fresh sample data and the null hypothesis was true.
At the tail end of the curve, the shaded area signifies alpha, which is also known as the critical region in statistics.
If the test result lies in the critical region of this curve, then it is considered statistically important and the null hypothesis is discarded. However, here in this picture, the conclusion is wrong, as the null hypothesis is actually correct in this situation.
How to reduce Type 1 Errors?
Let’s say you’re performing A/B testing flawlessly, then the best way to reduce type 1 errors is to increase the level of statistical significance. Needless to say, to get a higher level of statistical significance, you’ll require a larger sample size.
It is not very difficult to examine large sample sizes if you have massive traffic behind you. But in case your website fails to produce the desired traffic level, you will need to be more specific about the testing elements. First, you would need to determine what really attracts the viewers.
Below are the six ways to find the most significant element to focus on while testing.
Read user reviews and communicate with the customer support team: You need to evaluate and see what people actually think of your brand and service. Communicate with your sales, customer support, and product design departments to get a better understanding of what people really desire from you and your brand.
Look out why visitors leave your website without making any purchase orders. Use traditional analytics tools like Google Analytics to figure out why visitors are leaving your site without making any purchase orders. Compare this result with the conversion rate of the website to get a strong idea of which section you should focus on.
Identify the page elements that attract the audience: Use heatmaps to see exactly where or on what page elements users click, scroll, and focus their attention the most. Heatmaps will allow you to locate the trending elements, which will further help you to make a decision on which elements you should keep on, which ones to neglect, and which need additional assessment.
Collect feedback from customers. Conduct surveys and polls and incorporate feedback widgets on your website to allow your customers to send feedback about their personal experiences on the site. This will help you to understand the ongoing issues on your site and will assist you to prioritize what you need to fix to improve the overall performance of the website.
Check out Session Recording: Go through the session recording to see how the visitors have behaved on your website. Observe their browsing pattern thoroughly and see where they have struggled and how they are moving away from the site when they can’t locate what they require. Pay special attention and see what they exactly do just before leaving your website.
Usability Testing: Lastly, you can conduct usability testing to recognize how visitors observe and experience your website. This will provide you with feedback about issues that the visitors have encountered while visiting your site. Usability testing will also help you to find out what could actually improve their experience.
Final Thoughts
To reduce the chances of encountering a type 1 error by raising the implication level before making a decision, and conducting the testing over a longer period to accumulate more data. In contrast, there is no statistical method that can fully guarantee whether any version of a page is the best. Statistics can only provide a likelihood, not an assurance.
Does this mean that A/B tests are ineffective? That’s not true at all. Statistically, you still have a good chance of being correct for the majority of the time if you set a sufficient confidence interval despite the possibility of making a type 1 error. A precise confidence interval can decrease the danger of making a mistake in engineering, medical, and other fields, where absolute assurance is impossible.
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Simran works as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her.
