Sample Size When Conducting Statistical Analysis

Relevance or Irrelevance of Sample Size

Probability offers a tool or mechanism for public health professionals to help populations in making healthier decisions and choices. In this case, these officers examine different aspects for promoting the health of communities through the use probability to encourage better health choices. For instance, by educating the public that wearing a helmet when riding a bicycle may lessen the risk of severe head or brain injury by more than 80%, public health professionals are using probability to encourage better health choices in the population. Moreover, when carrying out statistical analyses, some experimental design factors might lead to the projection of the necessary sample size.

There are various factors that might contribute to sample size when conducting statistical analyses, especially because of experimental design factors. The major factors that contribute to sample size when conducting statistical analyses include the design of the experiment, the allowed standard deviation, the required power, and the anticipated effect size. These factors contribute to sample size by acting as strategies for determining sample size. The determination of sample size also requires comprehensive statistical calculations through the calculated margin of error. A projection of margin of error at 95% confidence level implies that there only a 5% chance of differing sample results from the actual population. This is usually presented by 1/N, where N is the sample size or number of participants in the study ("The Importance of Quality Sample Size," n.d.).

An important aspect when conducting statistical analyses is determining the relevance or irrelevance of sample size. The relevance or irrelevance of sample size in increasingly important when conducting statistical analyses given that performing a study on an entire population is impossible, though it's ideal. Therefore, researchers need to identify a relevant sample size that accurately represents and reflects the entire population. A relevant sample size helps in generating accurate results, which can be generalized or applicable to the entire population. In this case, the results of findings in a sample size would remain the same if a different sample size from a similar population is utilized.

However, the relevance or irrelevance of sample size is influenced by various factors when conducting statistical analyses. One of the factors that might result in the relevance or irrelevance of sample size during statistical analysis is the analytical plan. This is one of the considerations when making decisions regarding the sample size in relation to significance. The analytical plan influences the relevance or irrelevance of sample size by identifying the statistical significance and differences between groups. For example, when conducting comparative analysis between subgroups in the expected sample, the size is adjusted to identify the differences between these groups in relation to their statistical significance (Mora, 2010).

The second factor that might contribute to the relevance or irrelevance of sample size during statistical analysis is population variability, which is the diversity of the target population. If the target population demonstrates huge variability in attitudes and behaviors of the specific aspects being analyzed, a large sample size is required. This implies that the use of a large sample size is relevant while a small sample size is irrelevant for this study. For example, if 20 or 80% of the target population behaves in a certain manner, there is less variability as compared to if 50% would do so.