Final answer:
The sampling method in a factory producing car dashboard parts that would not represent a convenience sample could be a form of random sampling, such as simple random, stratified, cluster, or systematic sampling, which gives each part an equal chance of being selected and avoids biased data.
Step-by-step explanation:
When a factory checks a sample of parts to ensure correct dimensions for car dashboards, they can employ several sampling methods. A convenience sample is one where individuals are easily accessible but may lead to biased data. For instance, asking only the parts that are the easiest to reach or the first ones off the production line would be a convenience sample.
In contrast, random sampling methods, such as simple random sampling, stratified sampling, cluster sampling, and systematic sampling give each part an equal chance of being selected. A method that would not represent a convenience sample might involve selecting parts from different sections of the production randomly or using a systematic approach like checking every tenth part produced.
Cluster sampling could also be a non-convenience method if the factory is divided into clusters (e.g., different production lines or batches), and then samples are randomly selected from within those clusters. However, this method may not be as representative as a purely random sample, but it is more so than a convenience sample.