sytematic sample
Usually we are interested in the characteristics of large populations of items or people. It would often prove costly or impossible to measure these characteristics for the entire population. We therefore measure them for a carefully selected sample of the population and attempt to make scientific inferences about the entire population from the characteristics of the sample.
The variance decreases with a larger sample so that the sample mean is likely to be closer to the population mean.
The sample must have a high probability of representing the population.
Information obtained from the sample can be extrapolated to the whole population using statistics.
Using sample that does not match the population
Sample is subset of the population so sample size and population size is different.However, as a subset can be the whole set, if the sample size equals the population size, you have sampled the entire population and you will be 100% accurate with your results; it may cost much more than surveying a [representative] sample, but you get the satisfaction of knowing for what you surveyed the population exactly.Using a sample is a trade off between the cost of surveying the whole population and accuracy of the result.A census is a survey of the whole population and could be considered that the sample size = population size; in this case the results are 100% accurate.The television viewing figures are calculated using a sample of the whole population and then extrapolating them to the whole population; depending upon how the same was chosen, including its size, will affect the accuracy of the results - most likely not more than 95% accurate.With a carefully selected (that is properly biased) sample you can prove almost anything!
When the population standard deviation is known, the sample distribution is a normal distribution if the sample size is sufficiently large, typically due to the Central Limit Theorem. If the sample size is small and the population from which the sample is drawn is normally distributed, the sample distribution will also be normal. In such cases, statistical inference can be performed using z-scores.
Researchers are using a procedure known as simple random sampling. This involves selecting individuals at random, where every individual has an equal chance of being selected, to ensure the sample is representative of the population.
Inferential statistics is concerned with making predictions or inferences about a population from observations and analyses of a sample. That is, we can take the results of an analysis using a sample and can generalize it to the larger population that the sample represents. In order to do this, however, it is imperative that the sample is representative of the group to which it is being generalized.
To predict the number of females in the school, we can use the proportion of females from the sample. In the sample, 30 out of 75 students are female, which is 40%. Applying this percentage to the total student population of 1000, we estimate that there are approximately 400 females in the school (0.40 * 1000 = 400).
statistical inference
It is strangely worded like that, but the answer is yes.