Statistics Support Both Truth and Lies

Depending on how a data set is statistically analyzed, both truth and lies can be proven.

Posts about Statistics


Online textbook explaining statistics:

Posts about using statistics to conceal true results of studies:

Other posts about statistics used in Drug-War:


Examples


Some studies only report the differences in numbers, not the raw numbers or percentages.

For example, here are two hypothetical study results:

  1. Data A = 100 and data B = 150
  2. data A = 1,000,100. and data B = 1,000,200

The percentage of increase is much greater in the first case:

  • In the first case, it is an increase of 50%
  • in the second case, it is an increase of 00.01%

However, the amount of increase is far greater in the second case:

  • In the first case, it is an increase of 50
  • in the second case, it is an increase of 100.

In this way, you can “prove” that A is less than B or that A is greater than B.

You can publish either result because they both are technically correct,  but lead to opposite conclusions.


Another example of how to prove effectiveness by MD Whistleblower:

A drug named Profitsoar is tested to determine if it can reduce the risk of a heart attack.

Two thousand patients are participating in the study.  Each patients receives either Profitsoar or a placebo at  random.  Here are the results.

  • 1000 Profitsoar Patients: 4 heart attacks     
  • 1000 Placebo Patients: 6 heart attacks

As is evident, only 2 patients were spared a heart attack by the drug.   This is a trivial benefit as only 6 of 1000 patients in the placebo group suffered a heart attack.

This means that taking the drug provides no meaningful protection for an individual patient.

However, the drug companies will highlight the results in relative terms to package the results differently.

They will claim that Profitsoar reduced heart attack rates by 33%, which would lure many patients, and a few doctors to drink the Kool Aid.


 

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