About a year ago, ASA (American Statistical Association) published a series of articles urging journals and research investigators to ban the use of pvalues in their findings. You might be thinking, what is wrong with p-values?

What is Central Limit Theorem? Textbook definition of Central Limit Theorem states that “As sample size increases, the sampling distribution of the mean for an independent identically distributed variable will approximately resemble that of normal distribution regardless of its distribution”.

How do you explain confidence intervals intuitively? In any study,We all know we want to test our hypothesis to see if there is any difference between our assigned groups using some kind of experiment.

Technically, power is defined as the probability of obtaining statistically significant results (conventionally pvalue <0.05).Hypothesis test is conducted to gain statistical inferences from a study. For any study, we need to make sure that any null effect we are getting is not due to the fact that there were not enough participants enrolled in the study.

What is Sampling Distribution of the Sample Mean? We use sample mean as one of the measures of summary of variables in a study. In other words, sample mean is the average of the values observed.

dplyr is one of the commonly used packages in R for data manipulation.Following are major verbs used in dplyr for data curation and analysis: filter() select() summarize() arrange() mutate() We will look at use cases for these verbs with a simulated data:

Link to the paper https://www.ncbi.nlm.nih.gov/pubmed/8699212 The paper goes into in-depth discussion of how selecting variables based on significance achieved using pvalue criterion of less than 0.05 for use in multivariable analysis won’t be able to embrace the confounders sufficient to control for confounding.

Antifragile: Things that gain from Disorder By Nassim Taleb Link to the book https://www.amazon.com/Antifragile-Things-That-Disorder-Incerto/dp/0812979680 Anything that has more upside than downside from random events (or certain shocks) is antifragile; the reverse is fragile.

Seven Myths of Randomization Link to the paper https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5713 Link to the powerpoint presentation https://www.methodologyhubs.mrc.ac.uk/files/9214/3711/9501/Plenary-_Stephen_Senn.pdf This paper offers some valuable insights into some of the myths of randomisation that seem to be popular among researchers and investigators.

Link to the paper https://www.nejm.org/doi/full/10.1056/NEJMra1510064 Authors in this paper have made some valuable practical suggestions regarding how we want to look at the results of a trial if its primary outcomes fails to show what we expected.

Link to the paper https://www.nejm.org/doi/full/10.1056/NEJMra1601511 It is often the case that people tend to simplify positive results from a trial as a binary conclusion. However, positive results from primary findings do not guarantee everything done during the course of study was successful and well planned.