There are Goodness-of-Fit tests that can be used. The choice of test will depend on what is known about the population and sample data.
It means that particular observation is close to the population [or sample] mean.
A standard deviation for a sample makes a judgment on the whole data set whereas the population standard deviation uses the shole data set. If the questions says for example, a sample of 50 peoples height was taken... you would use the sample method but if you were asked : "Everyone in the class had their height measured" you could use the population method Hope that helps
I've included a couple of links. Statistical theory can never tell you how many samples you must take, all it can tell you the expected error that your sample should have given the variability of the data. Worked in reverse, you provide an expected error and the variability of the data, and statistical theory can tell you the corresponding sample size. The calculation methodology is given on the related links.
In general when you take a sample of values of a random variable you will find that those values lie around some central value that is characteristic of the total population for the random variable. A measure of central tendancy (such as a sample mean, sample mode or sample median) is a statistic which is intended to estimate the central value of the population using the values in the sample in some way.
Not if they are all members of the same team!
I believe you meant to ask: What distinguishes a random sample from a non random sample? A random sample means the selection or sampling from the population is by chance. Looking at the data, one might not be able to tell if the sample is random or selective. Consider a marketing survey which is included everytime you buy an item online. Random or non-random? It is a survey of recent customers, and probably a pretty good one. But it is not a random selection of all customers who have made purchases with clients.
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.
no.
it will be hard
they are two completely different things and can tell you different things.
1. determine the object 2. determine the study population 3.determine the data to be collected 4.collecting data 5.developing instrument 6.actual data gathering 7.data collation 8.summarize data 9.data presentation 10.data analysis
The sample comes from The SOS Band "Tell Me If You Still Care".