Basic Steps in Statistical Analysis

What is the first step in statistical analysis?

Formulating hypotheses and planning research design.

What does a hypothesis represent?

An educated guess about the results of an experiment or research based on observations.

What are independent variables?

Predictors in an experiment that are manipulated to observe their effect.

What are dependent variables?

Response or outcome variables that are measured in an experiment.

What is a simple hypothesis?

A hypothesis that describes the relationship between two variables.

What is a complex hypothesis?

A hypothesis that describes the relationship between more than two variables.

What is a logical hypothesis?

A hypothesis based on reasoning deduction without actual evidence.

What is an empirical hypothesis?

A working hypothesis that relies on concrete data.

What is a statistical hypothesis?

A hypothesis that tests a portion and generalizes the rest based on preexisting data.

What is a null hypothesis?

A hypothesis stating that there is no difference between groups or no relationship between variables.

What is an alternative hypothesis?

The hypothesis that contradicts the null hypothesis.

What are the characteristics of a good hypothesis?

Includes cause and effect, is testable, clearly defines variables, uses candid language, and adheres to ethics.

What is research design?

The overall strategy for data collection and analysis that determines the statistical tests to be used.

What is experimental design?

A research design that assesses cause-and-effect relationships using statistical tests.

What is correlational design?

A research design that explores relationships between variables without assuming causality.

What is descriptive design?

A research design that studies characteristics of a population or phenomenon.

What is a between-subjects design?

A design that compares outcomes of participants exposed to different treatments.

What is a within-subjects design?

A design that compares repeated measures from participants who have experienced all treatments.

What is a mixed (factorial) design?

A design where one variable is altered between subjects and another within subjects.

What is the difference between population and sample?

Population is the entire group of interest, while a sample is a specific group from which data is collected.

What is sampling error?

The difference between a population parameter and a sample statistic.

What is probability sampling?

A sampling method where every member of the population has a chance of being selected.

What is non-probability sampling?

A sampling method where some members of the population are more likely to be selected based on certain criteria.

What is simple random sampling?

A method where every member of the population has an equal chance of being selected.

What is systematic sampling?

A method where individuals are chosen at regular intervals from a numbered list of the population.

What is stratified sampling?

A method that divides the population into subpopulations and samples from each subgroup.

What is power analysis?

A technique to determine the sample size required to detect an effect with a given level of confidence.

What does the pwr package in R do?

It implements power analysis for various statistical tests.

What is effect size?

A standardized indication of how large the expected result of a study will be.

What is statistical power?

The probability of finding an effect that is present in the data.

What is the purpose of a one-way ANOVA power calculation?

To determine the sample size needed to detect an effect with a given significance level and power.

What does 'k' represent in an ANOVA test?

The number of groups being compared.

What are descriptive statistics?

Statistics that summarize and organize characteristics of a data set.

What are the three main types of descriptive statistics?

Distribution, central tendency, and variability.

What does frequency distribution describe?

The number of observations for each possible value of a variable.

What are the types of frequency distributions?

Ungrouped, grouped, relative, and cumulative frequency distributions.

What is the mean?

The sum of all values divided by the number of values.

What is the median?

The middle value of a data set when ordered from low to high.

What is the mode?

The most frequently occurring value in a data set.

When should you use the mean?

When data is at the interval or ratio level and normally distributed.

What is the range in statistics?

The difference between the highest and lowest values in a data set.

What is the interquartile range (IQR)?

The range of the middle 50% of the data set.

What does standard deviation measure?

The average distance between each value in a data set and the mean.

What is inferential statistics?

Making conclusions about population parameters based on sample statistics.

What are the two main methods of making inferences in statistics?

Estimation and hypothesis testing.

What is a point estimate?

A single value that represents the best guess of a population parameter.

What is a confidence interval?

A range of values that estimates where a population parameter lies.

What is the null hypothesis?

A statement that assumes no effect or no difference in the population.

What does a p-value indicate?

The likelihood of obtaining the observed results if the null hypothesis is true.

What are parametric tests?

Statistical tests that assume the data follows a normal distribution.

What are non-parametric tests?

Tests that do not assume a specific distribution for the data.

What is the purpose of regression tests?

To assess cause-and-effect relationships between variables.

What do correlation tests assess?

The relationships between variables without assuming causation.

What is a Type I error?

Rejecting the null hypothesis when it is actually true.

What is a Type II error?

Failing to reject the null hypothesis when it is false.

What is the purpose of a box plot?

To visualize a variable's central tendency, variability, and outliers.

What is the purpose of a histogram?

To visualize the distribution of a quantitative variable.

What is the purpose of a bar chart?

To compare the frequencies of different values for categorical variables.