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Inspecting an SRS of $20$ iPhones from $1000$ iPhones that are produced today is difficult.

A simple random sample (SRS) of size n is made up of $n$ people chosen from the population with an equal chance of being the sample that is actually chosen.

Today, the factory produces a thousand iPhones.

Assuming that no phones can be transported until the inspection is completed, checking a random sample of $20$ phones could cause the shipping process to be delayed. Additionally, the phones must be numbered in some fashion in order to create a random sample. It may be challenging to keep track of $1000$ phone orders.

The plan is to inspect the last $20$ iPhones made on that particular day. Bias is virtually always present in convenience sampling. The last $20$ iPhones produced are unlikely to be indicative of all iPhones produced throughout the day. It's likely that the quality of the iPhones manufactured varies amongst $1000$ iPhones created throughout the day. As a result, inspecting the last $20$ iPhones created is not a good sampling for the quality check of the day's manufacturing.

Another colleague proposes inspecting every fifteenth iPhone produced. A simple random sample $\left(SRS\right)$ of size n is made up of n people chosen from the population with an equal chance of being the sample that is actually chosen. Inspection of every $49th$ iPhone does not build an SRS since each sample of $20$iPhones has a different chance of getting chosen. The$49th,99th,...,999th$ iPhones will be sampled, whereas none of the others will be.