Will a sample descriptive statistics accurately estimate the underlying population's parameters?
Yes. Descriptive statistics are methods of organizing, summarizing, and presenting data in an informative way. Inferential Statistics (also called statistical inference) the methods used to estimate a property of a population on the basis of a sample.
The sample mean may differ from the population mean, especially for small samples.
There are 25C7 different samples of seven from a pool of 25.25C7 = 25!/(7!(25-7)!) = 480 700 different samples of 7
Yes you can.
There are a few reasons why samples are used in statistics. One reason is that the whole population cannot be used and a sample is a good representation of the whole.
Will a sample descriptive statistics accurately estimate the underlying population's parameters?
There is no inferential data. There is inferential statistics which from samples, you infer or draw a conclusion about the population. Hypothesis testing is an example of inferential statistics.
Descriptive statistics is the term given to the analysis of data that helps describe, show, or summarize data in a meaningful way such that patterns might emerge from the data. Inferential statistics are techniques that allow us to use population samples to make generalizations about the populations from which the samples were drawn.
There are 324,632 possible samples.
The answer depends on the population and is described by the sampling distribution of the mean.
Yes. Descriptive statistics are methods of organizing, summarizing, and presenting data in an informative way. Inferential Statistics (also called statistical inference) the methods used to estimate a property of a population on the basis of a sample.
The sample mean may differ from the population mean, especially for small samples.
different samples of respondents from the population complete the survey over a time period
There are 25C7 different samples of seven from a pool of 25.25C7 = 25!/(7!(25-7)!) = 480 700 different samples of 7
Yes you can.
The Central Limit Theorem (CLT) is a theorem that describes the fact that if a number of samples are taken from a population, the distribution of the means of the samples will be normal. This is true for all different distributions, whether or not the population is normal or something else. The main exception to this is that the theorem does not work particularly well if the samples are small (