Answer: (i) Outliers can distort the cluster centroids

K-means represents each cluster by its centroid, the arithmetic mean of the points assigned to it. The mean is highly sensitive to extreme values, so a single outlier lying far from the data bulk can pull a centroid toward itself, steal points from a genuine cluster, and inflate the within-cluster sum of squares — producing badly shaped or wrongly positioned clusters. Hence outlier detection (removal, or a robust variant such as K-medoids) is performed before and during clustering.

Outliers do not improve clustering accuracy (ii); they should not automatically be absorbed into clusters (iii); and the claim that they have no effect on clustering (iv) is false, precisely because mean-based centroids are sensitive to them.