A regression model exhibits lack-of-fit when it fails to adequately describe the functional relationship between the experimental factors and the response variable. Lack-of-fit can occur if important terms from the model such as interactions or quadratic terms are not included. It can also occur if several, unusually large residuals result from fitting the model.
Minitab displays the lack-of-fit test when your data contain replicates (multiple observations with identical x-values). Replicates represent "pure error" because only random variation can cause differences between the observed response values.
If the p-value is larger than α, you cannot conclude that the model does not fit the data well.