Related Experiment Video

Updated: Jul 26, 2026

Modified Drop Tower Impact Tests for American Football Helmets
07:08

Modified Drop Tower Impact Tests for American Football Helmets

Published on: February 19, 2017

Seat belt usage rates: a test of Peltzman's hypothesis

P S McCarthy

    Accident; Analysis and Prevention
    |October 1, 1986
    PubMed

    More Related Videos

    A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact07:30

    A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

    Published on: September 21, 2017

    Impact Mitigation in Modern Football Helmets: Advances and Limitations of Position-Specific Designs07:36

    Impact Mitigation in Modern Football Helmets: Advances and Limitations of Position-Specific Designs

    Published on: January 13, 2026

    Related Concept Videos

    Goodness-of-Fit Test01:16

    Goodness-of-Fit Test

    The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
    Expected Frequencies in Goodness-of-Fit Tests01:19

    Expected Frequencies in Goodness-of-Fit Tests

    A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
    Introduction to Test of Independence01:21

    Introduction to Test of Independence

    In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
    The test statistic for a test of independence is similar to that of a goodness-of-fit test:
    Hypothesis Test for Test of Independence01:16

    Hypothesis Test for Test of Independence

    The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
    H0: The two variables (factors)...
    Decision Making: P-value Method01:09

    Decision Making: P-value Method

    The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
    First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
    Sign Test for Median of Single Population01:20

    Sign Test for Median of Single Population

    In general, the sign test serves as a nonparametric method to test hypotheses about the median of a single population when the data does not follow a known distribution. This simplicity makes it particularly useful for small sample sizes or when the assumptions of parametric tests cannot be met. The process begins with identifying a null hypothesis, typically stating that the population median equals a specific value. The alternative hypothesis could be that the median is either not equal to,...