p-value
The P value, or probability value, is a statistical metric that indicates the significance of research findings. Researchers utilize P values to assess whether observed data trends are likely due to genuine effects or mere chance. By employing hypothesis testing, a method that evaluates the validity of assumptions about data, researchers can determine if their findings hold statistical significance. For instance, in an educational study where two groups of students are compared—one receiving tutoring and the other not—the P value helps ascertain whether any observed improvement in test scores is meaningful or coincidental. Generally, P values range from 0 to 1, with values below 0.05 typically regarded as statistically significant, implying a real difference between the groups studied. Conversely, values above this threshold suggest that any differences may be due to random variation. A P value of exactly 0.05 is considered ambiguous. Understanding P values is crucial for researchers in deciding whether to continue investigations or publish their results, as they reflect the reliability of their data analysis.
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P value
P value, or probability value, is a statistical value that helps indicate the statistical significance of data. Researchers use P values to help them determine whether it is likely that their analysis of data is correct and whether their research is important. P values can help researchers understand if they should continue to conduct research and whether they should publish their research.
Overview
P values are related to statistics, which is the science of collecting, analyzing, and interpreting data. Researchers collect and analyze data. When researchers see a trend in their data, they usually want to know whether the trend shows something important or whether the data merely shows a coincidence. Researchers can use the hypothesis test and P values to understand whether their data indicates a real trend or whether the data most likely shows a coincidence.
For example, two teachers think that their students will score better on tests if the students attend a tutoring program. The teachers together have eight classes of students. They pick a random sample of students from the classes. They designate half the group as Group A. The students in Group A will not change anything they are doing. It is the control group. They designate the other half of the students as Group B. Students in Group B attend the tutoring program. The teachers then readminister the tests they gave in the past. The teachers find that the students in Group A have the exact same average score they had in the past. They find that the students in Group B have an average score that is ten points higher than in the past.
The teachers collect the data about the groups. They believe that the change they observed in Group B’s test scores happened because of the tutoring program. Yet, they want to be sure that the change they observed in the data happened because of the tutoring program and not because of a random coincidence. The teachers conduct a hypothesis test, which is a test to determine the validity of the null hypothesis in a certain situation. The null hypothesis is the idea that two groups have no differences between them, and any differences noted between the groups happens because of random chance or because of researcher error. The teachers will use the P value to do the test.
P values measure between 0 and 1. Most researchers consider P values below 0.5 as statically significant and P values above 0.5 as statistically insignificant. A P value of exactly 0.5 could be either significant or insignificant. The teachers determine the P value is less than 0.5, which means the data is significantly significant. This disproves the null hypothesis and suggests that the trend in the data (the rise in student scores) is happen because of the variable (the tutoring program).
Bibliography
Beers, Brian. “P-Value.” Investopedia, 3 Jan. 2022, www.investopedia.com/terms/p/p-value.asp. Accessed 10 Feb 2022.
Dahiru, Tukur. “P – Value, a True Test of Statistical Significance? A Cautionary Note.” Annals of Ibadan Postgraduate Medicine, vol. 6, no. 1, 2008, pp. 21–6.
Denworth, Lydia. “A Significant Problem.” Scientific American, vol. 321, no. 4, 2019, pp. 62–7. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&db=f6h&AN=138756187&site=ehost-live. Accessed 10 Feb. 2022.
Gale, R., A. Hochhaus, and MJ Zhang. “What Is the (P-) Value of the P-Value?” Leukemia, vol. 30, 2016, pp. 1965–7, doi.org/10.1038/leu.2016.193. Accessed 10 Feb. 2022.
Rumsey, Deborah J. “How to Determine a P-Value When Testing a Null Hypothesis.” Statistics For Dummies, 13 July 2021, www.dummies.com/article/academics-the-arts/math/statistics/how-to-determine-a-p-value-when-testing-a-null-hypothesis-169062. Accessed 10 Feb 2022.
Rumsey, Deborah J. “What a P-Value Tells You about Statistical Data.” Statistics For Dummies, 6 July 2021, www.dummies.com/article/academics-the-arts/math/statistics/what-a-p-value-tells-you-about-statistical-data-169734. Accessed 10 Feb 2022.