Data reliability involves the determination of the extent
to which the data sample can address the question. For
data samples widely distributed over time, one can raise
the issue of the reliability of data taken with older
Data reliability is an issue when you can not measure a
phenomena directly but instead you have to use some tracer.
What is the best way to measure the extent of urban air pollution?
What is the best way to measure acid rain?
- Measure daily pollutants combined with some circulation
- Set up many measuring stations around the city? costly
- Use Lichen ?
Another good example of the issue of data reliability is
evidence for Global Warming:
Issues to raise:
- Is the determination of the world's average annual temperature
as a function of time the correct diagnostic for showing global
- Is the Northern Hemisphere Data more reliable than the Southern
Hemisphere Data? Is it a better diagnostic.
- Are measurements made 100 years ago as reliable as measurements
- Are the thermometers used located in the correct areas?
- Are their enough individual measurements to reliably establish
what the global mean temperature is?
Let's see the data:
Here is some Global
Data whose reliability is high. There is no doubt that CO_2 in
the atmosphere is increasing and is now at
Methane concentration from ice core data!
This shows strong exponential growth in the last 100 years or so -
consistent with growth in the world's population.