Discuss the use of p<0.05 to make statistical decisions. Is this something we should be doing? Why or why not? Support your decision with at least one additional reference.

The lesser the p-value the more evidence to reject the null hypothesis. P-value should be one of the things to be considered when doing and analyzing data in a research. Together with alpha, p-value must be less than alpha to say that it is statistically significant. When p-value is larger than alpha, then it is statically insignificant. Although getting the p-value gives us a broad analysis regarding a research, there are some which it doesn't give like if the two variables are related with each other. In other words, to make the result and data reliable, p-value alongside with different and appropriate test analysis should be performed. This will give us higher confidence and reliability that our research is true and follows the standard. Check out this research article: https://journals.lww.com/plasreconsurg/Abstract/2018/01000/Why_the_p_Value_Alone_Is_Not_Enough__The_Need_for.47.aspx

Going along with this summarization of how the significance level interacts with p-values and, by extension, testing for statistical significance, another thing to mention is how the significance level is set. For most experiments today, the significance level is set to be 0.05. This significance level has seemingly become the standard use for all experiments. One pitfall is this significance level should not fit all experimental design nor should it cover all circumstances. As a result, statistical conclusions can be based upon very arbitrary levels of significance and weaken the overall argument. Could you help to answer.