p<.031; 54 subjects do not provide the greatest support for the findings reported. Although p<.031 indicates statistical significance at the conventional level of p<.05, it is less convincing than p<.001 or p<.01, which indicate stronger evidence and higher confidence in the findings. Similarly, 54 subjects is a moderate sample size that may not have enough statistical power or precision to detect a true effect or difference between groups.
 

p=.301; 24 subjects do not provide any support for the findings reported. p=.301 indicates no statistical significance at any reasonable level of p<.05, p<.01, or p<.001. It means that there is a high probability (30.1%) of obtaining the observed result if the null hypothesis is true, and that there is no evidence to reject the null hypothesis and accept the alternative hypothesis. Furthermore, 24 subjects is a small sample size that may have low statistical power and high sampling error.
 

p<.01; 15 subjects do not provide the greatest support for the findings reported

p<.001; 73 subjects provide the greatest support for the findings reported. The p value, or probability value, tells you the statistical significance of a finding. It is the probability of obtaining a result as extreme or more extreme than the observed result if the null hypothesis is true. The null hypothesis is the assumption that there is no true effect or difference between groups in a study. A low p value indicates that the observed result is unlikely to occur by chance alone, and that there is evidence to reject the null hypothesis and accept the alternative hypothesis. The alternative hypothesis is the assumption that there is a true effect or difference between groups in a study. A common threshold for statistical significance is p<.05, which means that there is less than a 5% chance of obtaining the observed result if the null hypothesis is true. However, lower p values indicate stronger evidence and higher confidence in the findings. Therefore, p<.001 would provide more support for the findings than p<.01 or p<.031.


The sample size, or number of subjects, tells you how large and representative the sample is of the population of interest. The sample size affects the statistical power of a study, which is the ability to detect a true effect or difference between groups if it exists. The larger the sample size, the higher the statistical power and the lower the risk of making a false negative conclusion (Type II error). A larger sample size also reduces the sampling error and increases the precision and accuracy of the estimates. Therefore, 73 subjects would provide more support for the findings than 54, 24, or 15 subjects.


The other options are not correct because: