Week 1: Ingredients for Statistics Flashcards
(31 cards)
What are descriptive statistics?
Statistics that describe your data
What are inferential statistics?
Statistics that infer beyond your data and make decisions.
What do inferential statistics involve?
They involve making inferences about populations based on information from samples (as compared to descriptive statistics, which merely summarize known information)
What is the BIG picture?
Population -> Sampling -> Sample -> Statistic Inference -> Population
What is a simple random sample?
When each unit of the population has the same chance of being selected, regardless of the other units chosen for the sample.
Three comparisons between random and non random sampling?
- Random samples have averages that are centred around the correct number
- Non-random samples may suffer from sampling bias, and averages may not be centred around the correct number
- Only random samples can truly be trusted when making generalisations to the population.
What are population parameters?
A quantity or statistical measure that, for a given population, is fixed and that is used as the value of a variable in some general distribution or frequency function to make it descriptive of that population… A score from entire population such as the mean. They are usually unknown.
What letters do population parameters have?
Usually have greek letters.
What are statistics?
Statistics are figures from known data. They are scores of the sample only
What letters do statistics have?
They usually have roman letters.
What are point estimates?
They are single figure estimates of an unknown number. They will not match the population parameters exactly, but they are our bets guess given the data.
Example of how point estimates come about?
Using a single number (sample mean) to estimate another single number (population mean).
What do we use as a point estimate?
We use the statistic from a sample as a point estimate for a population parameter.
For a given sample statistic, what are plausible values for the population parameter? How much uncertainty surrounds the sample statistic?
It depends on how much the statistic varies from sample to sample.
What is the sampling distribution?
A hypothetical distribution. It is a probability distribution of a statistic obtained through a large number of samples drawn from a specific population.
How do we get a sampling distribution?
We select an infinite number of samples from a population and calculate a particular statistic (eg a mean) for each one. When we plot all these calculated statistics as a frequency histogram, we have a sampling distribution.
What does a sampling distribution show us?
A sampling distribution shows us how the sample statistic varies from sample to sample.
What is the central limit theorem (what does it state)?
The central limit theorem states that as the size of the samples we select increases, the nearer to the population mean will be the mean of these sample means and the closer to normal will be the distribution of the sample means. Eg, For random samples with a sufficiently large sample size, the distribution of sample statistics for a mean or a proportion is normally distributed.
What is the standard error?
The standard error of a statistic (SE) is the standard deviation of the sample statistic.
What does the standard error measure?
The standard error measures how much the statistic varies from sample to sample
What can the standard error be calculated as?
The standard error can be calculated as the standard deviation of the sampling distribution
What is the standard error formula?
(Write this out, it’s in condensed notes)
What is probability?
Expected relative frequency of a particular outcome.
What is outcome?
The results of an experiment
