2020 Plenary 2021-1: Statistical Literacy Across Disciplines
Wednesday, January 20, 2021
“Statistical Literacy Across Disciplines” organized by Dr. Richa Vatsa and chaired by Dr. Olawale Awe.
Our world is very well connected; it receives a vast amount of raw and processed information every day. However, not all information received is trustworthy, and hence, requires critical evaluation for the right decision making. Statistical literacy empowers one with a critical ability to collect, evaluate, and cross-check information/data, tables, numbers that surround us. It provides appropriate techniques to gather and understand the authenticity and quality of data, make possible inferences out of data, and make appropriate daily-life decisions based on the inferences. It can differentiate between real and fake news, lies or truth behind the viral information on web-platforms, and make us aware of manipulated data summaries used to lure ordinary people.
Statistical summaries bring logical reasoning to a discussion or a research study. To generate those summaries and cross-check them and make reasonable and appropriate decisions is statistical literacy for stakeholders such as government/business policymakers, decision-makers, journalists, and the most important common man.
This plenary session comprises three invited talks from statistical disciplines- survey sampling, statistical quality control (SQC), and Bayesian statistics. With the knowledge of these statistical disciplines, one can logically understand the appropriate techniques to gather data, perform quality-checks of output (data) generated, learn to deal with decision-making problems with uncertain options, and be aware of the system.
1. Dr. Asifa Kamal
“Generalized dual to exponential ratio type Estimator for finite population mean in the Presence of non-response”
2. Prof. Arun Kumar Sinha
“Control Charts and Capability Analysis for Statistical Process Control”
3. Dr. Richa Vatsa
“Understanding Uncertainty in Real Life Scenarios through the Concepts of Bayes’ Theorem and Bayesian Statistics”