Mastering Statistical Analysis in R

Front

What is the purpose of hypothesis testing in statistics?

Back

The purpose of hypothesis testing is to determine if there is enough evidence in a sample to infer that a certain condition holds for the entire population.

Front

Explain the difference between Type I and Type II errors.

Back

Type I error: false positive; Type II error: false negative.

Front

What is a p-value and how is it interpreted?

Back

A p-value is a measure of the probability that an observed result would occur by chance under the null hypothesis.

Front

Central Limit Theorem

Back

The Central Limit Theorem is a fundamental statistical principle that states that the means of sufficiently large samples from a population will be normally distributed, regardless of the population's distribution.

Front

What is the difference between parametric and non-parametric tests?

Back

Parametric tests assume a specific distribution; non-parametric tests do not.

Front

How do you interpret a confidence interval?

Back

A confidence interval indicates the range of values that likely contains the true population parameter.

Front

What is linear regression and when would you use it?

Back

Linear regression is used for predicting outcomes and understanding relationships between variables.

Front

Explain the concept of correlation and how it differs from causation.

Back

Correlation indicates a relationship between variables, while causation indicates a direct cause-and-effect relationship.

Front

What is ANOVA and when is it used in data analysis?

Back

ANOVA is a statistical method used to compare means among three or more groups.

Front

How can you check for normality in a dataset?

Back

Use visual methods (Q-Q plots, histograms) and statistical tests (Shapiro-Wilk, Kolmogorov-Smirnov) to check for normality.