1.12 for March 2017 or 2nd to last March.

[ScienceRocks]

1.12 for March 2017 or 2nd to last March....Last March was in the middle of the most powerful effin nino in recorded history.

Not bad....It is possible we could beat last year if any kind of nino forms later this year.

https://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt

 

[ScienceRocks]

I find it funny that we're in the 4th year for the record or runner up to the record and us warmers are losing.

Humans are stupid creatures that can't take a hint to save their own worthless lives.

 

[westwall]

I find it funny that we're in the 4th year for the record or runner up to the record and us warmers are losing.

Humans are stupid creatures that can't take a hint to save their own worthless lives.

What I find amusing is you fall all over yourself to post up "records" that they measure in hundredths of a degree when the best measuring devices they have available are only capable of measuring to one tenth of a degree. That means they are all lies matt. Or, "fake news" if you prefer....

 

[Crick]

You've never had the most basic class in statistics and probability. I thought you claimed to have a PhD in geology. Those two facts don't jibe well.

Increasing the Ability of an Experiment to Measure an Effect
Increasing the Ability of an Experiment to Measure an Effect
Sandra Slutz, PhD, Staff Scientist, Science Buddies
Kenneth L. Hess, Founder and President, Science Buddies

All experimental observations are a combination of signal, the true effect of a variable on an outcome, and noise, the random error inherent in your experimental technique. When designing and analyzing experiments, the goal is to maximize the signal-to-noise ratio so that you can draw accurate conclusions. Six common means of increasing the signal-to-noise ratio are:

1. Making repeated measurements of one item,
2. Increasing sample size,
3. Randomizing samples,
4. Randomizing experiments,
5. Repeating experiments, and
6. Including covariates.

How Sample Size Affects the Margin of Error - dummies
The relationship between margin of error and sample size is simple: As the sample size increases, the margin of error decreases. This relationship is called an inverse because the two move in opposite directions. If you think about it, it makes sense that the more information you have, the more accurate your results are going to be (in other words, the smaller your margin of error will get). (That assumes, of course, that the data were collected and handled properly.)

4. GENERAL SAMPLING CONSIDERATIONS
4.2.1 Sampling Accuracy

• Sampling accuracy is usually expressed as a relative index in percentage form (i.e. between 0 and 100%) and indicates the closeness of a sample-based parameter estimator to the true data population value.
• When expressed as a relative index, sampling accuracy is independent of the variability of the data population, i.e. data population parameters of high variability can still be estimated with good accuracy.
• When sample size increases and samples are representative, sampling accuracy also increases. Its rate of growth, very sharp in the region of small samples, becomes slower beyond a certain sample size.

 

[miketx]

Early statements about the great big hand in the air that causes global warming.

 

[westwall]

You've never had the most basic class in statistics and probability. I thought you claimed to have a PhD in geology. Those two facts don't jibe well.

Increasing the Ability of an Experiment to Measure an Effect
Increasing the Ability of an Experiment to Measure an Effect
Sandra Slutz, PhD, Staff Scientist, Science Buddies
Kenneth L. Hess, Founder and President, Science Buddies

All experimental observations are a combination of signal, the true effect of a variable on an outcome, and noise, the random error inherent in your experimental technique. When designing and analyzing experiments, the goal is to maximize the signal-to-noise ratio so that you can draw accurate conclusions. Six common means of increasing the signal-to-noise ratio are:

1. Making repeated measurements of one item,
2. Increasing sample size,
3. Randomizing samples,
4. Randomizing experiments,
5. Repeating experiments, and
6. Including covariates.

How Sample Size Affects the Margin of Error - dummies
The relationship between margin of error and sample size is simple: As the sample size increases, the margin of error decreases. This relationship is called an inverse because the two move in opposite directions. If you think about it, it makes sense that the more information you have, the more accurate your results are going to be (in other words, the smaller your margin of error will get). (That assumes, of course, that the data were collected and handled properly.)

4. GENERAL SAMPLING CONSIDERATIONS
4.2.1 Sampling Accuracy

• Sampling accuracy is usually expressed as a relative index in percentage form (i.e. between 0 and 100%) and indicates the closeness of a sample-based parameter estimator to the true data population value.
• When expressed as a relative index, sampling accuracy is independent of the variability of the data population, i.e. data population parameters of high variability can still be estimated with good accuracy.
• When sample size increases and samples are representative, sampling accuracy also increases. Its rate of growth, very sharp in the region of small samples, becomes slower beyond a certain sample size.

Cute bloviating. The facts are the most accurate measuring devices we have are accurate to .1 degree. Not .001. You CAN achieve greater accuracy but only within the confines of a laboratory. But never let a thing like a fact disrupt your idiocy.

 

[mamooth]

Cute bloviating. The facts are the most accurate measuring devices we have are accurate to .1 degree. Not .001. You CAN achieve greater accuracy but only within the confines of a laboratory. But never let a thing like a fact disrupt your idiocy.

That's not a fact. That's a conspiracy theory that you just yanked out of your ass.

But go on, Einstein, you seem to know more than the entire planet, explain to us your groundbreaking new theory.

Why do the laws of statistics, which are based only on mathematics, suddenly change if you move out of a lab?

And just how far out of the lab to you have to be? Is one step out the lab door enough?

To be a scientist, you have to know statistics cold. Almost all deniers would flunk Statistics 101. None of 'em could make it through any modern college science curriculum. They'd all fail physics, chemistry, statistics, everything. And then they'd declare it was because the professors were all socialists who were plotting against them.

 

[Crick]

You've never had the most basic class in statistics and probability. I thought you claimed to have a PhD in geology. Those two facts don't jibe well.

Increasing the Ability of an Experiment to Measure an Effect
Increasing the Ability of an Experiment to Measure an Effect
Sandra Slutz, PhD, Staff Scientist, Science Buddies
Kenneth L. Hess, Founder and President, Science Buddies

All experimental observations are a combination of signal, the true effect of a variable on an outcome, and noise, the random error inherent in your experimental technique. When designing and analyzing experiments, the goal is to maximize the signal-to-noise ratio so that you can draw accurate conclusions. Six common means of increasing the signal-to-noise ratio are:

1. Making repeated measurements of one item,
2. Increasing sample size,
3. Randomizing samples,
4. Randomizing experiments,
5. Repeating experiments, and
6. Including covariates.

How Sample Size Affects the Margin of Error - dummies
The relationship between margin of error and sample size is simple: As the sample size increases, the margin of error decreases. This relationship is called an inverse because the two move in opposite directions. If you think about it, it makes sense that the more information you have, the more accurate your results are going to be (in other words, the smaller your margin of error will get). (That assumes, of course, that the data were collected and handled properly.)

4. GENERAL SAMPLING CONSIDERATIONS
4.2.1 Sampling Accuracy

• Sampling accuracy is usually expressed as a relative index in percentage form (i.e. between 0 and 100%) and indicates the closeness of a sample-based parameter estimator to the true data population value.
• When expressed as a relative index, sampling accuracy is independent of the variability of the data population, i.e. data population parameters of high variability can still be estimated with good accuracy.
• When sample size increases and samples are representative, sampling accuracy also increases. Its rate of growth, very sharp in the region of small samples, becomes slower beyond a certain sample size.

Cute bloviating. The facts are the most accurate measuring devices we have are accurate to .1 degree. Not .001. You CAN achieve greater accuracy but only within the confines of a laboratory. But never let a thing like a fact disrupt your idiocy.

That's not what they said. You're lying.

 

[ScienceRocks]

Berkeley Earth have just updated their BEST figures. No prizes for guessing which year has the 2nd highest March value.

2016 +1.227 deg C
2017 +1.123 deg C
2002 +0.867 deg C
2010 +0.866 deg C
2015 +0.826 deg C

2016 & 2017 are streets ahead in terms of y-t-d anomaly.


http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt