Explanation
The p-value is a measure of how likely it is to observe a difference in conversion rates as large or larger than the one observed, assuming that there is no difference between the groups. A common threshold for statistical significance is 0.05, meaning that there is a 5% or less chance of observing such a difference by chance alone.
The table shows the p-values for each country, and we can see that only Germany has a p-value above 0.05 (0.13). This means that we cannot reject the null hypothesis that there is no difference in conversion rates between the test and control groups in Germany. Therefore, the increase in conversion from the new layout was not significant in Germany. For the other countries, the p-values are below 0.05, indicating that the increase in conversion from the new layout was statistically significant. Option A is correct.
Option B is incorrect because the increase in conversion from the new layout was significant in France (p-value = 0.002).
Option C is incorrect because it does not account for the variation across countries. While the overall conversion rate for the test group (8.4%) is higher than the control group (6.8%), this difference may not be statistically significant when we consider the country-specific effects.
Option D is incorrect because the new layout has the highest conversion rate in the United Kingdom (9.6%), not the lowest.
References:
P-value Calculator & Statistical Significance Calculator
p-value Calculator | Formula | Interpretation
How to obtain the P value from a confidence interval | The BMJ
Confidence Intervals & P-values for Percent Change / Relative Difference