Population distribution statistics are essential for understanding demographic trends and patterns within a given area. They inform resource allocation, urban planning, and policy-making by revealing how populations are spread across regions, which can affect infrastructure, healthcare, and education services. Additionally, these statistics aid in identifying social issues such as inequality and migration trends, allowing governments and organizations to address challenges effectively. Overall, they provide critical insights for strategic decision-making and sustainable development.
IQ is normally distributed in the general population. Age is not.
It is called a normal distribution.
I am under the assumption that in statistics, if the ten percent condition is not met, meaning that the sample size is more than 10% of the population, then the result is not a normal distribution.
The t distribution is a probability distribution that is symmetric and bell-shaped, similar to the normal distribution, but has heavier tails. It is used in statistics, particularly for small sample sizes, to estimate population parameters when the population standard deviation is unknown. The t distribution accounts for the additional uncertainty introduced by estimating the standard deviation from the sample. As the sample size increases, the t distribution approaches the normal distribution.
we prefer normal distribution over other distribution in statistics because most of the data around us is continuous. So, for continuous data normal distribution is used.
IQ is normally distributed in the general population. Age is not.
Why we prefer Normal Distribution over the other distributions in Statistics
In statistics, a quartile is each of four equal groups into which a population can be divided according to the distribution of values of a particular variable.
It is called a normal distribution.
Kenneth Hadden has written: 'The elderly population of Connecticut, 1980' -- subject(s): Older people, Social conditions, Statistics 'The population of Connecticut, 1970: age and sex composition' -- subject(s): Age distribution (Demography), Sex, Statistics, Vital Statistics 'The elderly population of Connecticut, 1970' -- subject(s): Older people, Statistics
I am under the assumption that in statistics, if the ten percent condition is not met, meaning that the sample size is more than 10% of the population, then the result is not a normal distribution.
The t distribution is a probability distribution that is symmetric and bell-shaped, similar to the normal distribution, but has heavier tails. It is used in statistics, particularly for small sample sizes, to estimate population parameters when the population standard deviation is unknown. The t distribution accounts for the additional uncertainty introduced by estimating the standard deviation from the sample. As the sample size increases, the t distribution approaches the normal distribution.
example of symmetrical distribution
Jacob S. Siegel has written: 'The demography and epidemiology of human health and aging' -- subject(s): Epidemiology, Health and hygiene, Age distribution (Demography), Population aging, Older people 'A generation of change' -- subject(s): Age distribution (Demography), Older people, Population, Social conditions, Statistics 'Demographic aspects of aging and the older population in the United States' -- subject(s): Older people, Statistics 'Coverage of the Hispanic population of the United States in the 1970 census' -- subject(s): Census, 19th, 1970, Hispanic Americans, Statistics 'The population of Hungary' -- subject(s): Population 'International trends and perspectives' -- subject(s): Age distribution (Demography), Aging, Older people, Statistics, Vital statistitics
μ is the symbol for the population mean in statistics. fyi and related but not necessary for the above answer: the sample mean is , enunciated by saying "x" bar. hope this helped. Citation : http://en.wikipedia.org/wiki/Arithmetic_mean
The question gives summary statistics for a population. If the underlying distribution is Gaussian, or some other known distribution, then the probability density function can be calculated. Even so, there is no question and so nothing to "solve".
Sampling distribution in statistics works by providing the probability distribution of a statistic based on a random sample. An example of this is figuring out the probability of running out of water on a camping trip.