- #1
GLD223
- 14
- 7
Is this a coincidence that this looks like a Gaussian?
- Thread starter GLD223
- Start date
-
- Tags
- Gaussian Statistics
In summary, the discussion revolves around the observation of a shape resembling a Gaussian distribution in a certain context, questioning whether this resemblance is merely coincidental or indicative of underlying patterns or phenomena. The analysis prompts consideration of statistical principles and the significance of Gaussian-like appearances in various datasets or situations.
- #2
Baluncore
Science Advisor
2023 Award
- 15,663
- 9,403
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
- 29,598
- 7,179
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
- 370
- 411
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
- 2,700
- 2,253
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
- 1,524
- 1,867
Too kurtotic to be Gaussian
FAQ: Is this a coincidence that this looks like a Gaussian?
What is a Gaussian distribution?
A Gaussian distribution, also known as a normal distribution, is a continuous probability distribution characterized by its bell-shaped curve. It is defined by two parameters: the mean (average) and the standard deviation (spread or width). Many natural phenomena and measurement errors tend to follow this distribution.
Why do many datasets appear to follow a Gaussian distribution?
Many datasets appear to follow a Gaussian distribution due to the Central Limit Theorem, which states that the sum of a large number of independent, identically distributed variables will tend to be normally distributed, regardless of the original distribution of the variables. This is why Gaussian distributions are commonly observed in various fields such as physics, biology, and social sciences.
How can I determine if my data follows a Gaussian distribution?
To determine if your data follows a Gaussian distribution, you can use statistical tests such as the Shapiro-Wilk test, Anderson-Darling test, or Kolmogorov-Smirnov test. Additionally, visual methods like Q-Q plots (quantile-quantile plots) can help you assess normality by comparing the quantiles of your data against the quantiles of a normal distribution.
What should I do if my data does not follow a Gaussian distribution?
If your data does not follow a Gaussian distribution, you can consider data transformations (such as log, square root, or Box-Cox transformations) to make it more normal. Alternatively, you can use non-parametric statistical methods that do not assume normality, such as the Mann-Whitney U test or the Kruskal-Wallis test.
Can a Gaussian distribution have different shapes?
While the basic shape of a Gaussian distribution is always bell-shaped, its specific appearance can vary based on the mean and standard deviation. A higher standard deviation results in a wider and flatter curve, while a lower standard deviation results in a narrower and taller curve. The mean shifts the center of the distribution along the horizontal axis.