Understanding A/B Testing Statistics to Get Higher Conversions
A/B testing is not just simple web testing technique and is more a game of “hits and misses” in a business environment. Even a slight irregularity or negligence can make your A/B testing fail. It is important to take appropriate precautions to make your A/B testing experiments successful for better conversions and sales. This web testing technique is much more mathematical than it is thought to be. The pivot point for the success of an A/B testing experiments is the acceptance of right level of statistical significance. If you are ignoring the standard threshold value for statistical significance, your A/B testing results are bound to suffer. Let us now understand the myths and importance of statistical significance in A/B testing environment for grabbing required conversions and sales. It gives validity to your A/B testing results for the final implementation on a site- It is a well known saying that “you can't shoot arrows in the dark” same is the case with the implementation of A/B testing results. You need to run your A/B tests for a scheduled time period to get a better idea about the confidence level or statistical significance achieved during the A/B testing experiments. Implementing the A/B tests before the minimum threshold statistical significance value of 95% will not provide you desired business results as expected during the start of split testing experiments. Statistical significance in A/B testing does not help in decision making- Getting an accepted level of statistical significance only gives legitimacy to your A/B testing experiments. It does not provide any idea about whether the testing results will hold good in the long term business plans. It may happen that your A/B testing results may fail in the changed business scenario, volume of site traffic or a change/addition of a product/services on the site. So, site owners can't bet on statistical significance for taking bold business decisions or reforms. Statistical significance is bound to change with a number of factors- It is not a matter of surprise that two different site owners who are implementing the same hypothesis for A/B testing experiments can arrive with different values of statistical significance. This may occur due to the change in the sample space taken, change in the testing duration, reliability of testing tool or other such factors. It is important to analyze all the limiting factors of
statistical significance to achieve more accurate A/B testing results. If you are not sure about the performance of your A/B testing tool, you can avail the amazing services of our reliable A/B testing tool like MockingFish for achieving expected business results. Statistical significance is bound to fluctuate during testing results- Since the starting of the A/B testing results till the final conclusion, the value of statistical significance is bound to change. To arrive at a permissible value of statistical significance, it is important to run your split testing for a scheduled time duration. The time duration should be realistic in nature but not less than 2- 3 weeks in order to judge the legitimacy of the testing results. If the value of statistical significance is found to be at par with the threshold value of 95%, it means that the testing results can be implemented on a website without any concern. It is important to pay adequate attention towards the statistical significance value while implementing A/B testing results for grabbing maximum conversions and sales. If you are not serious about the impact of statistical significance on A/B testing experiments, your business chances are bound to suffer a lot.