What is the null hypothesis in a Chi-square test for independence?
- The population means are equal
- The population variances are equal
- There is an association between the variables
- There is no association between the variables
The null hypothesis in a Chi-square test for independence states that there is no association between the variables - they are independent.
What is the relationship between the probability of a Type I error and the significance level of a test?
- It depends on the sample size
- There is no relationship
- They are directly proportional
- They are inversely proportional
The probability of a Type I error (false positive) is the same as the significance level of a test. A significance level of 0.05, for instance, means there's a 5% chance of rejecting a true null hypothesis (Type I error).
A __________ is a graphical representation used to observe and show relationships between two numeric variables.
- Bar chart
- Histogram
- Pie chart
- Scatter plot
A scatter plot is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. It's used to observe and show the relationship between two numeric variables.
The ________ distribution is used when there are exactly two mutually exclusive outcomes of a trial.
- Binomial
- Normal
- Poisson
- Uniform
A binomial distribution is used when there are exactly two mutually exclusive outcomes of a trial (often referred to as a success or a failure). It models the total number of successes in a fixed number of independent trials.
What are the assumptions for conducting a Kruskal-Wallis Test?
- All of the above
- Data must be normally distributed
- Samples must be independent
- Variances must be equal
The key assumption for conducting a Kruskal-Wallis Test is that the samples must be independent.
The _______ measures the variability of the point estimate.
- Mean
- Median
- Mode
- Standard error
Standard error is a measure of the statistical accuracy of an estimate, equal to the standard deviation of the theoretical distribution of a large population of such estimates.
Converting ________ data into quantitative data involves the process of coding.
- Continuous
- Discrete
- Qualitative
- Quantitative
Converting Qualitative data into quantitative data involves the process of coding. This process involves assigning numerical values to qualitative information (such as categories or themes) so that they can be manipulated and analyzed statistically. For example, if you have data on types of pets (dogs, cats, etc.), you can assign a numerical code (1 for dogs, 2 for cats, etc.) to transform this qualitative data into quantitative data.
What is a key difference between parametric and non-parametric statistical methods?
- The amount of data they can handle
- The assumptions they make about the data distribution
- The speed at which they analyze data
- The type of variables they can analyze
The key difference between parametric and non-parametric statistical methods is the assumptions they make about the data distribution. Parametric methods assume that the data follow a certain distribution, while non-parametric methods do not make these assumptions.
If the assumptions of a parametric test are violated, it might be appropriate to use a ________ statistical method.
- biased
- non-parametric
- normal
- parametric
If the assumptions of a parametric test are violated, it might be appropriate to use a non-parametric statistical method. Non-parametric methods have fewer assumptions and can be used with different types of data.
What is the concept of the standard error in relation to a sampling distribution?
- It is the mean of the population
- It is the mean of the sampling distribution
- It is the standard deviation of the population
- It is the standard deviation of the sampling distribution
The standard error is a statistical term that measures the standard deviation of the sampling distribution. In other words, it provides a measure of the variability or dispersion of sample means around the population mean. A smaller standard error indicates that the sample mean is likely to be a closer approximation to the population mean.