Greta Thunberg

TheRemainsOfTheDave said:
The adjustments are made when information comes to light AFTER the model is developed. You cannot report on data you know is incorrect because it is flawed and not reporting actuality so you have to go back and recalculate results with what is known or believed to be correct readings. This is completely normal behaviour in data analysis, everywhere. This is my area of expertise but I accept that people might not understand how this works if they've not worked in this field.
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Personally mate, I think you're talking nonsense. The only justification for "adjusting" data is if evidence comes to light about how incorrect assumptions were made when raw data was collected. That should be entirely independent of whether there are any climate models and/or how the data happens to fit. And adjusting data retrospectively simply when climate models don't fit, is suspicious, to say the least. And my field was atmospheric physics which I studied at Imperial, BTW. But if you think you know better, I lose no sleep.
 
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Chippy_boy said:
Personally mate, I think you're talking nonsense. The only justification for "adjusting" data is if evidence comes to light about how incorrect assumptions were made when raw data was collected. That should be entirely independent of whether there are any climate models and/or how the data happens to fit. And adjusting data retrospectively simply when climate models don't fit, is suspicious, to say the least. And my field was atmospheric physics which I studied at Imperial, BTW. But if you think you know better, I lose no sleep.
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I'll try to make this as simple as I can.
They had a dataset that returned results over a long period of time.
They discovered some of the equipment that generated some of the data was not to be trusted due to the equipment not having proper calibration routines.
Raw data from the affected equipment was removed from the dataset.
The results change because of this. If I have 100 data values and calculate an average, and then remove ten data values the average is probably going to be different.

No knowledge of what the raw data means is required. Perhaps my 20 year career analysing data in many sectors was a dream!
 
TheRemainsOfTheDave said:
I'll try to make this as simple as I can.
They had a dataset that returned results over a long period of time.
They discovered some of the equipment that generated some of the data was not to be trusted due to the equipment not having proper calibration routines.
Raw data from the affected equipment was removed from the dataset.
The results change because of this. If I have 100 data values and calculate an average, and then remove ten data values the average is probably going to be different.

No knowledge of what the raw data means is required. Perhaps my 20 year career analysing data in many sectors was a dream!
Click to expand...
Yes perhaps it was.
 
TheRemainsOfTheDave said:
I'll assume that you've chosen to end this discussion in this manner as you accept the rest of my post.
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Well you assume wrongly. As I explained to you, I am qualified in this area myself (unlike you it would seem) so your bullshit does not wash with me and you are not worth wasting my time on. You can carry on bullshitting others all you like.
 
Chippy_boy said:
Well you assume wrongly. As I explained to you, I am qualified in this area myself (unlike you it would seem) so your bullshit does not wash with me and you are not worth wasting my time on. You can carry on bullshitting others all you like.
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Ah well, can't say I tried. Despite giving a mathematically proven example you believe I'm bullshitting!

Have a good Xmas.
 
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