R Programming for Statistics and Data Science - The P-Value

The video tutorial introduces hypothesis testing, focusing on the concept of P-values. It explains how P-values are used to determine the smallest level of significance at which a null hypothesis can be rejected. The tutorial covers the calculation of P-values using Z scores and probability tables, and how to compare them with alpha levels to make decisions in hypothesis testing. It also discusses the application of P-values in statistical software and their importance in research. The lesson concludes with a brief mention of the next topic, testing for the mean with unknown variance.

What happens to the P value when conducting a two-tailed test compared to a one-tailed test?

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Discuss the implications of a P value being close to zero in statistical analysis.

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