You and your team are tasked with helping to determine whether an incremental new feature should be added to an existing product. The question is whether the adding the new feature will increase the number of units sold of the product. The data you have available are from other products the company sells that include this same feature.

You and your team are tasked with helping to determine whether an incremental new feature should be added to an existing product. The question is whether the adding the new feature will increase the number of units sold of the product. The data you have available are from other products the company sells that include this same feature.

Because your company is a global company, it is able to release new features to different parts of the world at different times. You determine that a simple analysis that could be done would be to look across all the company’s markets and compare units sold between those markets that have the new feature and those that don’t adjusting for potentially confounding differences between the various markets.

An analyst on your team builds a prediction model from the data and notes to you that the inclusion of the new feature only explains about 2% of the variation in the number of units sold (i.e. it is a weak predictor) and that there are many other factors that better predict the number of units sold.

What conclusion can you draw from this?