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Calcule a significância do teste AB com nossa ferramenta online gratuita. Obtenha resultados instantâneos com explicações úteis e dicas para um melhor entendimento.
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Calculadora de Significância de Teste AB
Calcule a significância do teste AB com nossa ferramenta online gratuita. Obtenha resultados instantâneos com explicações úteis e dicas para um melhor entendimento.
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O que é um A/B Test Significance Calculadora?
An A/B Test Significance Calculator helps you determine if the difference in conversion rates between two variations of a webpage or app is statistically significant. It tells you whether the observed results are likely due to the changes you made or just random chance.
Como usar
1. Enter the number of visitors for the Control (original) version. 2. Enter the number of conversions for the Control version. 3. Enter the number of visitors for the Variant (new) version. 4. Enter the number of conversions for the Variant version. 5. Select your desired confidence level (usually 95% or 99%). 6. The calculator will show you the conversion rates, uplift, and whether the result is significant.
Perguntas frequentes
What is an A/B Test Significance Calculator?
This tool helps you determine if the difference in performance between two variations (Control and Variant) is statistically significant or just due to random chance.
What is statistical significance?
Statistical significance is a measure of probability that the observed difference between your control and variant is not caused by random chance. A common threshold is 95% confidence.
What is a P-value?
The P-value represents the probability of seeing results as extreme as yours if there was actually no difference between the two versions. A P-value less than 0.05 usually indicates statistical significance.
What is the difference between one-tailed and two-tailed tests?
A one-tailed test checks if the Variant is better than the Control (directional). A two-tailed test checks if the Variant is simply different from the Control (either better or worse). Two-tailed is more conservative and common.
Why does my result say 'Not Significant' even if Variant B looks better?
This usually means your sample size is too small. While Variant B has a higher conversion rate, the difference is not large enough or the traffic is not high enough to rule out luck.
What confidence level should I choose?
The industry standard is 95%. This means you accept a 5% risk of concluding there is a difference when there actually isn't (a false positive). Use 99% for stricter testing.
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