# To P or not to P?

M. Drew LaMar
April 29, 2016

“Increased quantification of scientific research and a proliferation of large, complex datasets in recent years have expanded the scope of applications of statistical methods.” From Wasserstein, Ronald L., and Nicole A. Lazar. “The ASA's statement on p-values: context, process, and purpose.” The American Statistician (2016).

### Class announcements

• Reading assignment for today is posted (you have till midnight)
• Final Exam: Thursday, May 5, 9 am-12 noon
• Designed for 2 hours
• Cumulative
• 8-10 questions
• 3 questions from Chaps. 16-18
• Know the same formulas as you needed for Exam #3
• Projects
• Solutions to HW will be up tomorrow

### Design and Statistics

“Appropriately chosen techniques, properly conducted analyses and correct interpretation of statistical results also play a key role in ensuring that conclusions are sound and that uncertainty surrounding them is represented properly.”

### Importance

“The issues touched on here affect not only research, but research funding, journal practices, career advancement, scientific education, public policy, journalism, and law.”

### What is a P-value?

“Informally, a p-value is the probability under a specified statistical model that a statistical summary of the data … would be equal to or more extreme than its observed value.”

Definition: A $$P$$-value is the probability of obtaining a statistical summary of the data or worse given that the null hypothesis is true.

### Gotta have principles

Principle #1: P-values can indicate how incompatible the data are with a specified statistical model.

• The most common context is a model, constructed under a set of assumptions, together with a so-called â€śnull hypothesis.“
• The null hypothesis postulates the absence of an effect.
• This incompatibility can be interpreted as casting doubt on or providing evidence against the null hypothesis or the underlying assumptions.

### Gotta have principles

Principle #2: P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.

• The P-value is a statement about data in relation to a specified hypothetical explanation, and is not a statement about the explanation itself.
• Remember, a P-value is the probability of getting a specific statistical summary of the data or worse assuming the null hypothesis is true.

### Gotta have principles

Principle #3: Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.

• A conclusion does not immediately become “true” on one side of the divide and “false” on the other.
• Researchers should bring many contextual factors into play to derive scientific inferences:
• the design of a study,
• the quality of the measurements,
• the external evidence for the phenomenon under study,
• and the validity of assumptions that underlie the data analysis.

### Gotta have principles

Principle #4: Proper inference requires full reporting and transparency.

• P-values and related analyses should not be reported selectively.
• Researchers should disclose:
• the number of hypotheses explored during the study,
• all data collection decisions,
• all statistical analyses conducted,
• and all p-values computed.

### Gotta have principles

Principle #5: A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.

### Gotta have principles

Principle #6: By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.

• A p-value near 0.05 taken by itself offers only weak evidence against the null hypothesis.
• A relatively large p-value does not imply evidence in favor of the null hypothesis; many other hypotheses may be equally or more consistent with the observed data.

### Now what?

Alternatives:

• Estimation (e.g. confidence intervals)
• Bayesian methods
• alternative measures of evidence, such as likelihood ratios or Bayes Factors
• decision-theoretic modeling
• false discovery rates

### The choice is yours

Remember to use statistics for good not evil!