ALEX Classroom Resource

  

P-Value as a Benchmark in Experimental Research | Prediction by the Numbers

  Classroom Resource Information  

Title:

P-Value as a Benchmark in Experimental Research | Prediction by the Numbers

URL:

https://aptv.pbslearningmedia.org/resource/nvpn-sci-pvalue/p-value-as-a-benchmark-in-experimental-research-prediction-by-the-numbers/

Content Source:

PBS
Type: Audio/Video

Overview:

Learn about the origins and meaning of “p-value,” a statistical measure of the probability that has become a benchmark for success in experimental science, in this video from NOVA: Prediction by the Numbers. In the 1920s and 1930s, British scientist Ronald A. Fisher laid out guidelines for designing experiments using statistics and probability to judge results. He proposed that if experimental results were due to chance alone, they would occur less than 5 percent (0.05) of the time. The lower the p-value, the less likely the experimental results were caused by chance. Use this resource to stimulate thinking and questions about the use of statistics and probability to test hypotheses and evaluate experimental results.

Content Standard(s):
Mathematics
MA2015 (2016)
Grade: 9-12
Algebra I
47 ) Understand that two events A and B are independent if the probability of A and B occurring together is the product of their probabilities, and use this characterization to determine if they are independent. [S-CP2]

Mathematics
MA2015 (2016)
Grade: 9-12
Algebra II
40 ) Understand the conditional probability of A given B as P(A and B)/P(B), and interpret independence of A and B as saying that the conditional probability of A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B. [S-CP3]

Mathematics
MA2015 (2016)
Grade: 9-12
Algebra II
42 ) Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations. [S-CP5]

Example: Compare the chance of having lung cancer if you are a smoker with the chance of being a smoker if you have lung cancer.

Mathematics
MA2015 (2016)
Grade: 9-12
Algebra II
43 ) Find the conditional probability of A given B as the fraction of B's outcomes that also belong to A, and interpret the answer in terms of the model. [S-CP6]

Mathematics
MA2015 (2016)
Grade: 9-12
Precalculus
45 ) Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. [S-IC2]

Example: A model says a spinning coin falls heads up with probability 0.5. Would a result of 5 tails in a row cause you to question the model'

Mathematics
MA2015 (2016)
Grade: 9-12
Precalculus
46 ) Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. [S-IC3]

Mathematics
MA2015 (2016)
Grade: 9-12
Precalculus
48 ) Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant. [S-IC5]

Mathematics
MA2015 (2016)
Grade: 9-12
Precalculus
49 ) Evaluate reports based on data. [S-IC6]

Mathematics
MA2015 (2016)
Grade: 9-12
Algebra II with Trigonometry
44 ) Understand the conditional probability of A given B as P(A and B)/P(B), and interpret independence of A and B as saying that the conditional probability of A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B. [S-CP3]

Mathematics
MA2015 (2016)
Grade: 9-12
Algebra II with Trigonometry
46 ) Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations. [S-CP5]

Example: Compare the chance of having lung cancer if you are a smoker with the chance of being a smoker if you have lung cancer.

Mathematics
MA2015 (2016)
Grade: 9-12
Algebra II with Trigonometry
47 ) Find the conditional probability of A given B as the fraction of B's outcomes that also belong to A, and interpret the answer in terms of the model. [S-CP6]

Tags: conditional probability, data, independence, independent, outcomes, parameters, probability, pvalue, randomized experiment treatments, reports, statistics
License Type: Custom Permission Type
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AccessibilityVideo resources: includes closed captioning or subtitles
Comments
  This resource provided by:  
Author: Hannah Bradley