A larger sample size increases the level of significance and confidence in the results of a hypothesis test. By increasing the number of data collection recordings and performing the test across a wider range, the more clearly a trend emerges and easily identified. This removes or lessens the probability that the trend is merely a false observation or anomaly which can be caused by an unrecognized skew in a smaller sample size (Kalla, 2009). One tried and true example of this is the final inspection and laboratory testing that I perform on a daily basis which is deeply rooted in the long-upheld methodology of the American Society of Testing and Materials (ASTM). When dealing with production lots of rolled goods that are several thousand yards in length and multiple feet in width, a viable sampling method must be created or adhered to so that an accurate representation of the lot may be generated rather than a quick "snapshot" which does not fully represent the level of variation that exists within the production run (Cordon, 1966). If only a small sample size is selected then a product may be passed at inspection which contains a significant portion of failing material, leading to return goods, customer dissatisfaction, and monetary reimbursement for both the product and the shipping cost. Conversely, if a product is inspected properly and in accordance with textile standards, then this risk is reduced significantly as the likelihood of identifying, isolating, and removing the negatively affected area increases dramatically. please discuss and support with references