Descriptive vs Inferential Stats

What is the main goal of descriptive statistics?

• To analyze causal relationships

• To conduct hypothesis testing

• To predict future outcomes

• To summarize and describe the main features of a dataset.

Give an example of a measure of central tendency.

• Variance

• Mean

• Mode

• Median

Explain the difference between population and sample in inferential statistics.

• Population refers to animals, while sample refers to plants.

• Population is used in biology, while sample is used in chemistry.

• Population is always larger than sample.

• Population is the entire group of interest, while a sample is a subset of the population used for analysis.

What is the purpose of inferential statistics?

• To bake cookies

• To perform surgery

• To make predictions or inferences about a population based on a sample of data.

• To study historical events

Define standard deviation in descriptive statistics.

• Standard deviation measures the central tendency of a dataset.

• Standard deviation is calculated by summing all values in a dataset.

• Standard deviation is a measure of the dispersion of a set of values from their mean.

• Standard deviation is the same as the mean of a set of values.

When would you use a histogram in data analysis?

• When summarizing data with a single value.

• When comparing two groups of data.

• When analyzing categorical data.

• When visualizing the distribution of a continuous variable.

What is the central limit theorem and why is it important in inferential statistics?

• The central limit theorem is only applicable when the sample size is very small.

• The central limit theorem is used to calculate probabilities for non-normal distributions.

• The central limit theorem states that the sample mean will always be equal to the population mean.

• The central limit theorem is important in inferential statistics because it allows us to make inferences about a population mean using the sample mean, even if the population distribution is not normal.

Describe the process of hypothesis testing in inferential statistics.

• The process of hypothesis testing involves guessing randomly

• Calculating test statistics is not part of hypothesis testing

• Hypothesis testing does not require collecting data

• The process of hypothesis testing in inferential statistics includes formulating hypotheses, selecting a significance level, collecting data, calculating test statistics, determining the p-value, and making a decision.

How do you calculate the mean of a dataset?

• Divide by the sum of the values

• Add all values in the dataset, then divide by the total number of values.

• Subtract the largest value from the smallest value

• Multiply all values in the dataset

Why is it important to understand the difference between descriptive and inferential statistics?

• Understanding statistics is not important for data analysis

• It is important to understand the difference between descriptive and inferential statistics to know when to use each type for accurate data analysis and decision-making.

• Descriptive and inferential statistics provide the same results

• Using both types of statistics leads to confusion