Statistical Analysis
Basic Concepts in Statistical Analysis
Standard Deviation
Four Aspects should be Considered before using Statistical Analysis
Ref: https://bloomingtontutors.com/blog/when-to-use-the-z-test-versus-t-test
- Whether we are working with a mean (for example, “37 students”) or a proportion (e.g., “15% of all students”).
- Whether or not we know the population standard deviation (σ).
- Whether or not the population is normally distributed. This is mainly important when dealing with small sample sizes.
- The size of our sample. The magic number is usually 30 — below that is considered a “small” sample, and 30 or above is considered “large”. When the sample size is large, the central limit theorem tells us that we don’t need to worry about whether or not the population is normally distributed.
Proportion problems are never t-test problems — always use z!
Different statistical methods
Ref: https://study.com/academy/lesson/z-test-t-test-similarities-differences.html
Both z-tests and t-tests require data with a normal distribution.
Z-test: Z-tests are statistical calculations that can be used to compare population means to a sample’s. The z-score tells you how far, in standard deviations, a data point is from the mean or average of a data set. A z-test compares a sample to a defined population and is typically used for dealing with problems relating to large samples (n > 30). Z-tests can also be helpful when we want to test a hypothesis. Generally, they are most useful when the standard deviation is known. (need more study about z-test)
ttest: ttest is most useful when we need to determine if there is a statistically significant difference between two independent sample groups. Usually, t-tests are most appropriate when dealing with problems with a limited sample size (n < 30).
