2.7 KiB
(Ch 19, stat 1040)
| Term | Definition |
|---|---|
| Qualitative | A descriptive value (red, blue, high, low) |
| Quantitative | A numerical value (7, 8, 9) |
| Population | The entire set of existing units that investigators wish to study |
| Sample | A portion or subset of the population |
| Parameter | A number that describes a characteristic of a population (10% of US senators voted for something) |
| Statistic | A number that describes a sample characteristic (71% of Americans feel that ...) |
A global consumer survey reported that 6% of US taxpayers used or owned cryptocurrency in 2020. The US government is interested in knowing if this percentage has increased. The University of Chicago surveys 1,004 taxpayers and finds that 13% have used or owned crypto in the past year (2021)
In the above example:
- The population was US taxpayers
- The parameter was 6%
- The sample was 1004 taxpayers
- The statistic was 13%
An ideal sample will represent the whole population.
Sampling
| Sample Type | Description |
|---|---|
| Simple random | Advantages: - Procedure is impartial - Law of Averages Disadvantages - Not always possible - Can be very expensive |
| Quota Sampling | Attempts to get certain proportions based on key characteristics. Quota sampling doesn't guarantee that the selection is an accurate representation. |
| Cluster Sampling | Divide population into subgroups, randomly select a subgroup, and sample all of the subjects in that group |
| Convenience | Sampling done near to the researcher because it's easier. |
Simple Random Samples
Bias
| Bias Type | Description |
|---|---|
| Selection | When the procedure that selects the sample is biased |
| Non-Response | Those that don't respond to a survey may have different characteristics than those that do respond |
| Response | When the question is worded in a leading way to elicit a certain response. |
| Volunteer response | Self selecting, individuals volunteer to answer |
| Measurement | Interviewing method influences the response, uses loaded words or ambiguities. |
| (Ch 20, stat 1040) |
The expected value for a sample percentage equals the population percentage. The standard error for that percentage = (SE_sum/sample_size) * 100%
To determine by how much the standard error is affected, if n is the sample size, the standard error changes by \frac{1}{\sqrt{n}}
Accuracy in statistics refers to how small the standard error is. A smaller standard error means your data is more accurate.
You can use the below equation to find the percentage standard error of a box model that has ones and zeros. the % of ones and zeros should be represented as a proportion (EG: 60% = 0.6).
\sqrt{\frac{(\% of 1)(\% of 0)}{num_{draws}}}