See the section on quantitative surveys for further discussion on populations and samples.We make inferences (conclusions) about a population from a sample taken from it, therefore it is important that population and sampling is well understood, as any error will influence your inferences (conclusions).
The use of statistical tests (as detailed above) will provide you with valuable findings if you know how to interpret the results and use them to inform your research.
A variable is any measured characteristic or attribute that differs for different subjects.
There are two types of statistics: The general idea of statistical analysis is to summarise and analyse data so that it is useful and can inform decision-making.
You would analyse descriptive statistics if you wanted to summarise some data into a shorter form, where as, you would use inferential statistical analysis when you were trying to understand a relationship and either generalise or predict based on this understanding.
In some situations we can examine the entire population, then there is no inference from a sample.
In statistics, a result is called statistically significant if it is unlikely to have occurred by chance.
The independent variable answers the question “What do I change?
”, the dependent variable answers the question “What do I observe?
This confidence interval would fall to 0.6 if the survey returned a value of 99% or 1%.
It is important that the survey sample size is considered for statistics where 50% of the population answer both ‘yes’ and ‘no’ as this is when the confidence level is broadest and so provides the general level of accuracy for a sample.