The Role of Sample Size on Interpretation of the Result in Applied ResearchA Study on the Analysis of Regression Models

Many applied types of research have been carried out using statistical hypothesis tests and many researchers reject or accept the research hypothesis only by looking at the p-value. On the other hand, due to the relationship between the p-value and the sample size, in large samples, despite the small size of the work, the hypothesis is accepted with very high confidence, and the researcher relies solely on the p-value to support practical insignificance results. Thus, many research studies can be classified as research that has many tests of meaningful assumption but lacks practicality and scientific significance. In this paper, first, considering the issue of big data, the issue of data volume and data diversity in big data from a statistical perspective, then the p-value, effect size, and confidence interval as three decision criteria in hypothetical tests on different samples in the range (173-19361) The impact of big data on these three indicators has been examined and in particular. The results showed: A large sample is not an advantage to increasing reliability in hypothetical tests. It can make meaningful claims that are not of practical importance and in small samples fall into the category of random effects and sampling error. Also, the effect size is not affected by the sample size and converges with increasing constant sample size. Finally, the data showed that the confidence interval is visually better than other indicators.