Not if the attribute recorded in qualitative rather than quantitative. If you ask a group of people what their favourite fruit is, a mean of their answers has no meaning.
It is a value calculated from the sample values only.It is a value calculated from the sample values only.It is a value calculated from the sample values only.It is a value calculated from the sample values only.
The standard deviation of the sample means is called the standard error of the mean (SEM). It quantifies the variability of sample means around the population mean and is calculated by dividing the population standard deviation by the square root of the sample size. The SEM decreases as the sample size increases, reflecting improved estimates of the population mean with larger samples.
The Coefficient of Variation is a ratio showing the degree to which individual points of data in a sample deviate from the mean. It is calculated by taking the standard deviation of the sample and dividing that by the mean of the sample. It can be useful for comparing different data sets because it is a ratio (or percentage) and not an absolute number.
"The advantage is that the mean takes every value into account. A disadvantage is that it can be affected by extreme values. " The mean or more properly the "arithmetic mean" of a sample will eventually approximate the mean of the distribution of the population as the sample size increases. If the population distribution is skewed (not symmetrical), the mode and median will not provide an estimate of the mean, even as the sample size becomes large.
The sample mean may differ from the population mean, especially for small samples.
The population mean is the mean calculated over every member of the set of subjects being studied. It is usually not available and a survey is used to find an estimate for the population mean. The mean value of the variable in question, calculated from only the subjects included in the sample (or survey) is the sample mean. Provided some basic statistical requirements are met, the sample mean is a "good" estimate of the population mean.
Standard error of the sample mean is calculated dividing the the sample estimate of population standard deviation ("sample standard deviation") by the square root of sample size.
It is a value calculated from the sample values only.It is a value calculated from the sample values only.It is a value calculated from the sample values only.It is a value calculated from the sample values only.
i mean conclucion
It means that every member of the population has the same probability of being included in the sample.
It means that the every element in a population has an equal chance of being selected to be in the sample which is studied. Equivalently, in considering a sample of a particular size, every possible sample of that size has the same chance of being selected.
The standard deviation of the sample means is called the standard error of the mean (SEM). It quantifies the variability of sample means around the population mean and is calculated by dividing the population standard deviation by the square root of the sample size. The SEM decreases as the sample size increases, reflecting improved estimates of the population mean with larger samples.
The Coefficient of Variation is a ratio showing the degree to which individual points of data in a sample deviate from the mean. It is calculated by taking the standard deviation of the sample and dividing that by the mean of the sample. It can be useful for comparing different data sets because it is a ratio (or percentage) and not an absolute number.
A numerical value calculated for a sample is called a descriptive statistic.
Your question is a bit difficult to understand. I will rephrase: In hypothesis testing, when the sample mean is close to the assumed mean of the population (null hypotheses), what does that tell you? Answer: For a given sample size n and an alpha value, the closer the calculated mean is to the assumed mean of the population, the higher chance that null hypothesis will not be rejected in favor of the alternative hypothesis.
The single quantity compared to an entire sample is called a statistic. It is a numerical measurement calculated from the data in the sample, such as the mean, median, or standard deviation. The statistic provides insight into the characteristics or properties of the sample as a whole.
A statistic is a value calculated from a data sample. For example, the mean (average) is a statistic. You calculate the mean by adding up the values of the data you have and dividing by the number of values. Usually, you want to know the corresponding value for the whole population but this is impossible to obtain in practice. So you have to use a statistic calculated from a sample. For eaxmple, if you want to know the height of sixteen year old students in the UK, you cannot measure every single student. So you measure a sample (selected randomly), and calculate the average height of the sample. This is a statistic. See: http://www.stats.gla.ac.uk/steps/glossary/basic_definitions.html#stat