Part 4 - Auctions & Statistical Basics Flashcards
What do you need to consider when stating statistical hypotheses?
- Identify the null and alternative hypotheses
- Decide on an appropriate significance level
- One or two-tailed tests?
What is the confidence interval?
Confidence is defined as 1 minus the significance level
– When we construct a 95% confidence interval, we are saying that we are 95% certain that the true population mean is covered by the interval
What is the significance level?
“chance that we are wrong” = higher significance level means higher chance of rejecting H₀, but also greater error chance
with experiments: keep in mind low sample size, therefore greater significance level might make sense
What 5 steps do you go through with hypothesis testing?
- State statistical hypotheses
- Check assumptions
- Calculate test statistic
- Evaluate the statistic
- Interpret your results
What is a two-tailed test?
Non-directional (two-tailed) - the direction of deviation of the alternative case is not specified
What is a one-tailed test?
Directional hypotheses (one-tailed) - the direction of deviation from the null value is clearly specified; a specific predicted outcome is stated
What is a type I error?
- Falsely reject H₀
- Denoted by α = chance of making an error
What is a type II error?
- Fail to reject H₀ when H₁ is true
- Denoted by β = power of statistical test
What do you do when checking the assumptions of a statistical test?
- check assumptions for the particular test to use
- more violations mean less confidence in the final p-value
- Measurement Level = type of variables
- Independence of Observations = we usually assume randomized, independent respondents
What types of variables are there?
- Nominal = categories
- Ordinal = ordered categories -> interval data = with certain ranges
- Numeric
Parametric vs. Nonparametric Test?
- nonparametric: not high statistical power, but more robust to violations
Assumptions for Kolmogorov-Smirnov Test?
– Random sampling is assumed, as with all significance tests
– Continuous interval or ratio data are required for the Kolmogorov-Smirnov goodness-of-fit test for exact results
–Ordinal data or grouped interval data may be (and commonly are) used if approximate results are sufficient
What is the Kolmogorov-Smirnov Test for A Single Sample?
- Tests whether the distribution of a variable obtained using a sample matches a given distribution (e.g. is a variable normally distributed)
- Non parametric and distribution free (makes no assumption about the distribution)
- Observed distribution - the distribution of the variable in the sample
- Hypothetical distribution - the expected distribution of a variable with the same parameters if it conformed to a particular type of distribution
What are metric variables?
with a numerical value, i.e. ratios, numbers, intervals
What is homogeneity of variance?
that two samples have the same distibution