The aim of this study was to search the theoretical and practical aspects of hypothesis testing, experimental errors and statistical decisions, ways to decrease these errors, stressing the difference between statistical significance and meaningfulness of findings and the importance of population parameters. Results showed that statistical inferences could be affected by errors of statistical decision of type one, alpha, and type two, beta and experimental errors. The researcher determines the probability of errors of type one and two whereas experimental errors are rooted in human, environmental and methodological procedures and their value is determined by standard deviation. The comparison of statistic-related P value and alpha indicates statistical significance. Meaningfulness of statistic is calculated by shared variances among variables. Type one error deprives the population of useful information while type two error flows false information into the literature. Considerable experimental errors which can decrease the validity of findings are characterized by standard deviation. Failure in the estimation of population parameters obscures the status of the variable of interest in the population. This study was an attempt to provide some suggestions to reduce errors of interpretation and conclusion of experimental studies, to boost the meaningfulness of experimental findings and to estimate population parameters.