• Like
Research quality & data reliability b.v.raghunandan
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Research quality & data reliability b.v.raghunandan

  • 287 views
Published

deals with lack of quality in modern day research and the poor reliability of data generated

deals with lack of quality in modern day research and the poor reliability of data generated

Published in Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
287
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
3
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Research Quality and Data Reliability -B.V.Raghunandan, SVS College, BantwalPost-Graduate Department of Economics, St.Aloysius College, Mangalore August 2, 2012
  • 2. Research• Advancement of Discipline• Relation between Variables• Basis for Policy Framing• Initially, divorced from market and commercialisation• Soundly entrenched in Philosophy• Product of Thinking rather than a product emerging from Laboratory
  • 3. Trend-Changers• Invention of a Laboratory• Structured Research• Topical Research• Too many Assumptions• Marketable Research• Irrelevant Study• Vested Interests
  • 4. Emergence of Statistics• By the Turn of Twentieth Century• Averages, Dispersion, Correlation and Regression and their removal from Reality• Correlation and Causation Differentiated Initially, but are treated the Same Now• Sample was regarded with suspicion initially, but now only samples are used• Convenience Sample
  • 5. Exponential Growth of Statistics• Every Discipline Adopted It• Sample Surveys became the Order of the Day• Convenience Sample Became Dominant• Governments’ Information Needs• Usage of Computers increased the Capacity of Data Processing• Research by Pharmaceutical Companies• More Precautions in Healthcare and Clean Surrounding
  • 6. Forecasting & Risk Management• Probability used for Forecasting• Standard Deviation and Beta came to be used to Measure the level of Risk• Usage of Derivatives for Risk Management
  • 7. Aftermath• Human Judgement Sidelined• Holistic Approach became Rare• Creation of Unnecessary Alarms• Predictions Became Meaningless• Fear became Predominant• Medicines became Complex• Natural Immunity is lost• Unnecessary and Unwanted Information
  • 8. Dangers• People Started Dying out of Medicines and Treatment Rather Than Due to The Ailment• A Totally Artificial Life• Unrealistic Benchmarks of Development• Man became a Bigger Parasite• Information Did not Lead to Knowledge• Man has Become a Contradiction of Nature• Research Aimed at Enslavement People• Band Wagon Mentality
  • 9. Contribution of Market • Commodities Market lead to Incurable Inflation • Stock Market Caused Bankruptcy of Individuals, Companies and Governments • Popularising Unnecessary Goods and Services • Introduction of Harmful Products • Usage of Undesirable Process of Production • Highly Dangerous Preservatives and Raw Materials • Cost and Profit have Priority over the Customers • Hedonistic Culture
  • 10. Perversions of Society• People Living in a Reported World• People Losing Sense of Right and Wrong• Confusion due to Support for Contradictory Principles• Universal Presence of Prejudiced Data• Inability to Understand the Completeness of the Data• Giving Research Tag to Statistical Surveys
  • 11. Questioning the Theories• In 1973, ‘A Random Walk Down Wall Street’ by Burton G. Malkiel• “Developments in the market are at random and unpredictable”• “It is impossible to outperform the market without assuming additional risk”• “past can not be a guide to the future”
  • 12. Black Swan of Nassim Nicholas Taleb • error of comparing real- world randomness with the "structured randomness” • Impossibility of possessing all the information • Small unknown variations in data can have a major impact • Flawed theories based on empirical data that is insufficient
  • 13. Reinventing Research & Data• Question the Premises• Comparison of Forecasting and Actuals• Determining the Validity of Gregorian Calendar• Questioning the Length of Period of Study• Study Abandoned in Case of Availability of Insufficient Data• Holistic Approach• Reducing the Importance of Samples
  • 14. Accountability of Research Institutes• Publishing Failure in Prediction• Maintaining a Track Record of Failures• Determining the Percentage of Failure on which the Model Has to be Changed• Public Knowledge of Methodology Used• Penalty for Vested Interest Findings• Penalty for Avoidable Doomsday Prediction
  • 15. Thank You