Business Insider makes a common error in proclaiming that Belgium's higher GDP growth rate is "bringing up the Eurozone GDP growth rate average". It lists a bunch of countries in order of growth rates, making the case that the big countries like France, Germany, UK and Italy were laggards by comparison.
If Belgium is bringing up the Eurozone growth rate average, then Germany also is bringing up the average, despite its growth rate being 1/7th that of Belgium. In fact, the data show that the contribution of Belgium to GDP growth of Eurozone is essentially identical to the contribution of Germany. The reason is that Germany's economy is 7 times that of Belgium. So, each unit of growth by Germany has 7 times more "power" than each unit of growth in Belgium.
In fact, Europe should thank Italy because it contributed almost as much as Germany and Belgium combined. While Italy's growth rate was half that of Belgium, its economy is 4 times as big.
Comparing the growth rates of individual countries is misleading. It is much better to think in terms of proportion of contribution to the whole (in million Euros). We can start with this table:
Many countries have not reported their latest quarterly numbers so we can't continue from here. However, if all the data were available, we would now sum the column labelled "contribution" and then compute the proportion of contribution by each country. This metric should be used to evaluate which countries are "bringing up the Eurozone average".
This example falls into the category of "error in spirit, not necessarily error in arithmetic". An analyst can reasonably protest - and many do in similar situations - that it is a mathematical certainty that all else equal, an increase in a component of the average will bring up the average. This type of thinking is analogous to that which proclaims an effect to be worth talking about because it is "statistically significant" even though the size of the said effect is so small as to render it practically meaningless.
Consider this: the elevator gets stuck with you inside it. The engineers are called in. The first thing they notice is the ugly rust on the door as the paint has come off. Instead of inspecting the motor, which is much more likely to improve your experience, they return to their office to obtain fresh paint. Getting distracted by insignificant features of a data set while missing the big picture is a common pitfall in data analysis.