The Power of Descriptive and Inferential Statistics

📊Descriptive statistics summarize data sets by calculating measures of central tendency like the mean, median, and mode.

🔍Descriptive statistics also include measures of variance like the range and standard deviation.

📈Inferential statistics are used to make predictions and generalize conclusions from samples to populations.

🧪Examples of inferential statistics include correlation coefficients and t-tests.

🔬Statistical significance is calculated to determine the probability that a result occurred due to chance.

What are descriptive statistics?

—Descriptive statistics summarize data sets by calculating measures of central tendency and variance.

What is the difference between descriptive and inferential statistics?

—Descriptive statistics summarize data, while inferential statistics make predictions and generalize conclusions from samples to populations.

What are some examples of descriptive statistics?

—Examples of descriptive statistics include the mean, median, mode, range, and standard deviation.

What are some examples of inferential statistics?

—Some examples of inferential statistics are correlation coefficients and t-tests.

How is statistical significance determined?

—Statistical significance is determined by calculating the probability that a result occurred due to chance.

00:00Scientists use data for organizing, describing, and making inferences or predictions.

00:15Descriptive statistics summarize data sets by calculating measures of central tendency.

00:30Measures of central tendency include the mean, median, and mode.

01:41Descriptive statistics also include measures of variance like the range and standard deviation.

02:31Inferential statistics are used to make predictions and generalize conclusions from samples to populations.

02:44Examples of inferential statistics include correlation coefficients and t-tests.

02:56Statistical significance determines the probability that a result occurred due to chance.

03:03Practical significance is determined by the effect size and real-world usefulness of the findings.