During the virtual forest tutorial, I used three major sampling strategies. Stratified sampling, cluster sampling, and simple random sampling. All these were probability-based sampling methods, and I used them to improve the reliability of the research because the probability-based sampling strategies give information that reflects the characteristics of the population. However, based on the three sampling strategies, the technique with the fastest estimated sampling time was simple random sampling because I would randomly select the sample with no measurements.
Additionally, simple random sampling had the lowest percentage error for the two rarest species. Using the percentage error formula, the simple random sampling technique had an error rate of 30%; however, stratified sampling had a percentage error of 42%, while cluster sampling had a percentage error of 43%. As a result, the best sampling strategy was simple random sampling. The accuracy differed with the species abundance because when some common species were selected, there was a drastic reduction in the percentage error between the three sampling methods. However, simple random sampling superseded the othersin all cases as it had the least sampling percentage error.
Your post is missing some details. You don’t list the error for both of the rare species or say what they are or what the common species are. What were your sampling times, they are usually pretty close demonstrating that haphazard sampling isn’t much faster than random or other strategies.