Whether a model is significant or non-significant means whether the relationship between variables in the data is due to chance or something else: A model is non-significant if the p-value is greater than 0.05. This means that the observed difference could be due to chance more than one in twenty times. A model is considered significant if the p-value is less than 0.05, and non-significant if the p-value is greater than 0.05. The significance level is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference

If lack of fit is not showing whether the model is significant or non-significant, it typically means that the model does not adequately capture the relationship in the data. In other words, the model's predictions are not significantly different from the actual data points. It may be necessary to reconsider the model choice, check for potential outliers.