When we reject the null hypothesis, we conclude that b) there is not enough statistical evidence to infer that the null hypothesis is true.
This means that our data has provided sufficient evidence to support the alternative hypothesis. In hypothesis testing, the null hypothesis typically represents a statement of no effect or no difference, while the alternative hypothesis represents a statement indicating the presence of an effect or a difference. By rejecting the null, we are essentially saying that the data we collected contradicts the assumption made by the null hypothesis and suggests that the alternative hypothesis is more plausible based on the evidence at hand.