- #1
GLD223
- 14
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Is this a coincidence that this looks like a Gaussian?
- Thread starter GLD223
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- Gaussian Statistics
- #2
Baluncore
Science Advisor
2023 Award
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Welcome to PF.
Yes, it is a coincidence.
It looks like moisture is wicking through the render, with a mould or algae growing there.
Is this the cool, shaded side of the building?
What city?
Yes, it is a coincidence.
It looks like moisture is wicking through the render, with a mould or algae growing there.
Is this the cool, shaded side of the building?
What city?
- #3
sophiecentaur
Science Advisor
Gold Member
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Nearly every natural relationship between variables (within some arbitrary range) either looks linear, quadratic, exponential, sinusoidal or gaussian. Change the scales of the x and y axes an you can get a 'convincing fit' (good enough, often to convince a jury).GLD223 said:
Don't blame the Scientist who starts off with one of those curves when trying to work out the theory; it's always a good first step.
- #4
Haborix
- 341
- 375
I made a habit out of annoying my experimentalist friends by asking them "Is that a Gaussian?!" every time they were looking at data.
- #5
Hornbein
- 2,085
- 1,703
It's not a Gaussian. Too pointy.
I once saw a Gaussian when snowflakes leaked through a slot onto a narrow ledge.
I once saw a Gaussian when snowflakes leaked through a slot onto a narrow ledge.
- #6
BWV
- 1,465
- 1,781
Too kurtotic to be Gaussian
Related to Is this a coincidence that this looks like a Gaussian?
1. Is it just a coincidence that this data looks like a Gaussian distribution?
No, it is not just a coincidence. The Gaussian distribution, also known as the normal distribution, is a commonly observed pattern in nature and is often seen in various data sets due to its mathematical properties.
2. Why does the Gaussian distribution appear so frequently in different scientific fields?
The Gaussian distribution appears frequently in different scientific fields because it represents a natural pattern that emerges in many systems. It is a result of the central limit theorem, which states that the sum of a large number of independent random variables approaches a normal distribution.
3. Can non-Gaussian data also be modeled using a Gaussian distribution?
Yes, non-Gaussian data can be approximated using a Gaussian distribution through techniques such as data transformation or fitting methods. However, it is important to consider the limitations of using a Gaussian model for non-Gaussian data.
4. How can we determine if a data set follows a Gaussian distribution?
There are various statistical tests and visual methods that can be used to determine if a data set follows a Gaussian distribution, such as the Shapiro-Wilk test, Kolmogorov-Smirnov test, Q-Q plots, and histograms. These tools can help assess the normality of the data.
5. What are the implications of assuming a Gaussian distribution in data analysis?
Assuming a Gaussian distribution in data analysis can simplify calculations and make certain statistical methods more applicable. However, it is important to validate this assumption and consider the potential biases that may arise if the data deviates significantly from a Gaussian distribution.
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