Related Experiment Video

Updated: Jun 17, 2026

A Common Marmoset Model of Mother-Infant Intervention for Breastfeeding Disorders in the Presence of Paternal Inhibition and Maternal Neglect
05:04

A Common Marmoset Model of Mother-Infant Intervention for Breastfeeding Disorders in the Presence of Paternal Inhibition and Maternal Neglect

Published on: September 22, 2023

The relationship between breastfeeding test weights and postpartum breastfeeding rates

Susan Wilhelm1, T Kim Rodehorst-Weber, Mary Beth Flanders Stepans

  • 1University of Nebraska Medical Center College of Nursing, Scottsbluff, Nebraska 69361, USA.

Journal of Human Lactation : Official Journal of International Lactation Consultant Association
|December 18, 2009
PubMed

More Related Videos

Assessment of the Efficacy of An Osteopathic Treatment in Infants with Biomechanical Impairments to Suckling07:11

Assessment of the Efficacy of An Osteopathic Treatment in Infants with Biomechanical Impairments to Suckling

Published on: February 5, 2019

An Efficient Single-Person Technique for Milk Sampling from Laboratory Mice04:56

An Efficient Single-Person Technique for Milk Sampling from Laboratory Mice

Published on: March 28, 2025

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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).
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.