Statistics Concepts and Hypothesis Testing

The video provides an introduction to statistics, focusing on data types, distributions, sampling, estimation, hypothesis testing, and p-values. Using NBA examples, it explains categorical and numerical data, probability distributions, and the importance of sampling. The video also covers hypothesis testing, including null and alternate hypotheses, and discusses p-values and the issue of p-hacking in research.

What are the two main classes of data in statistics?

• Sample and Population

• Categorical and Numerical

• Discrete and Continuous

• Nominal and Ordinal

Which type of data has no inherent order among its categories?

• Ordinal

• Nominal

• Discrete

• Continuous

What type of data is represented by Steph Curry's three-point percentage?

• Discrete Numerical

• Proportion

• Ordinal

• Nominal

Which distribution is commonly referred to as a bell curve?

• Uniform Distribution

• Bimodal Distribution

• Normal Distribution

• Skewed Distribution

What is the purpose of a confidence interval in statistics?

• To calculate the mean

• To determine the sample size

• To estimate the range of a parameter with a certain level of confidence

• To prove a hypothesis

In hypothesis testing, what does the null hypothesis represent?

• The hypothesis that the sample size is too small

• The hypothesis that there is no effect

• The hypothesis that the sample is biased

• The hypothesis that there is an effect

What is the role of the alternate hypothesis in hypothesis testing?

• To determine the sample size

• To calculate the p-value

• To provide evidence against the null hypothesis

• To confirm the null hypothesis

What does a p-value measure in hypothesis testing?

• The mean of the sample

• The probability of the null hypothesis being true

• The probability of the sample statistic being extreme if the null hypothesis is true

• The sample size required for the test

What is p-hacking in scientific research?

• Collecting data to support a pre-determined hypothesis

• Testing multiple hypotheses and only reporting the significant ones

• Using a large sample size to ensure significance

• Ignoring the null hypothesis

Why is p-hacking considered problematic in research?

• It ensures the null hypothesis is always rejected

• It proves the alternate hypothesis

• It reduces the sample size

• It increases the likelihood of Type I errors