BSB123 Lecture Notes - Lecture 6: Central Limit Theorem, Simple Random Sample, Sampling Distribution
Data Analysis – Sampling Distribution
- Using samples is less costly, less time consuming, always possible, and decreases destructive
tests
- Inferential Statistics
oStatistical inferencing is the method for drawing conclusions about a population
based on a sample taking into account sampling error
oTwo types of inferencing
Hypothesis testing
Statistical method of determining the strength of evidence against
the hypothesised value of a population parameter of interest
Estimation
Using sample data to estimate the true value of a population
parameter
- Sample statistics and population parameters
oParameter – numerical descriptive measure obtained from a population
oStatistic – numerical descriptive measure derived from a sample
- Simple random sample
oSample in which each individual member of the population has an equal chance of
being selected in the sample
- Sampling Distribution of the sample mean
oSampling distribution – probability distribution of a sample statistic
oDistribution of all the sample means that can be obtained from the population
oIf a random sample of n is drawn from a population, the sample mean is a random
variable
oIf the parent population is normal, the sample mean is also normally distributed
oThe sample mean has a smaller spread than the parent population
oWhen the sample size increases, the spread of the sample mean decreases
oSample standard deviation
Population standard deviation/Square root of (n)
oStandard error
Standard deviation of the sample means
Deviation and error can mean the same thing
find more resources at oneclass.com
find more resources at oneclass.com
Document Summary
Using samples is less costly, less time consuming, always possible, and decreases destructive tests. Inferential statistics: statistical inferencing is the method for drawing conclusions about a population based on a sample taking into account sampling error, two types of inferencing. Statistical method of determining the strength of evidence against the hypothesised value of a population parameter of interest. Using sample data to estimate the true value of a population parameter. Sample statistics and population parameters: parameter numerical descriptive measure obtained from a population, statistic numerical descriptive measure derived from a sample. Simple random sample: sample in which each individual member of the population has an equal chance of being selected in the sample. Population standard deviation/square root of (n: standard error. Deviation and error can mean the same thing. These must be calculated in order to draw inferences about the sample distribution: example a market research firm is interested in the distribution of annual household income (x).
