What are p-values?? Seriously.

The paragraph introduces the concept of p-values using the analogy of flipping a coin. It explains that the intuition required to understand p-values is already present in people, as demonstrated by their reactions to the outcome of 100 coin flips. The speaker, Justin Zeltzer, uses the example of flipping 90 heads out of 100 times to show that such an extreme outcome would lead people to question the fairness of the coin. This is similar to how p-values are used in statistics to determine the likelihood of observing sample results under the null hypothesis, which is the default position that there is no effect or difference.

This paragraph delves deeper into the definition and calculation of p-values. It explains that a p-value is the probability of obtaining a sample as extreme as, or more extreme than, the observed sample under the null hypothesis. The example continues with the coin flip scenario, illustrating how a sample of 56 heads out of 100 would have a p-value of 0.193, or 19.3%, if the coin was fair. The paragraph also introduces the concept of a two-tailed p-value, which considers both directions of deviation from the expected value (e.g., more than 56 heads or fewer than 44 heads).

The paragraph discusses the historical development of p-values, tracing back to 1710 when John Arbuthnot noticed a higher male birth rate than female over 82 years. Although Arbuthnot did not use the term p-value, his work is seen as an early application of the concept. The paragraph then moves to the 19th century, discussing the work of Simeon Poisson and Ronald Fisher, who further developed the concept of p-values and established the 0.05 threshold for statistical significance.

This section explains the difference between one-tailed and two-tailed p-values. It describes that a two-tailed p-value considers both directions of deviation from the null hypothesis (e.g., more or fewer heads than expected), while a one-tailed p-value focuses on one direction only (e.g., only more heads or only fewer tails). The paragraph provides examples of scenarios where each type of p-value would be used, emphasizing that the choice between one-tailed and two-tailed p-values depends on the nature of the research question and whether the direction of the effect is of interest.

The paragraph presents a practical example of how p-values are used in research, specifically in a study published in the Medical Journal of Australia about epilepsy management using a smartphone application. It explains how p-values are used to compare baseline characteristics between groups to ensure they are similar and to analyze outcome differences between the groups. The paragraph highlights the significance of low p-values in indicating a true difference between groups and how high p-values confirm that baseline characteristics are not significantly different.

In the concluding paragraph, the speaker summarizes the main points discussed in the video and encourages viewers to share the content if they found it informative. Justin Zeltzer invites viewers to subscribe, like, and comment on the video to support the growth of the channel and enable the creation of more educational content. He also directs viewers to his website for access to other videos in the series.