Related Experiment Video

Updated: Jul 28, 2026

Standards for Quantitative Metalloproteomic Analysis Using Size Exclusion ICP-MS
09:51

Standards for Quantitative Metalloproteomic Analysis Using Size Exclusion ICP-MS

Published on: April 13, 2016

IUBMB updates Ph.D. standards

E J Wood1, F Vella

  • 1School of Biochemistry and Molecular Biology, University of Leeds, Leeds, U.K.

Bioessays : News and Reviews in Molecular, Cellular and Developmental Biology
|August 5, 2000
PubMed

More Related Videos

Standardizing a Non-Lethal Method for Characterizing the Reproductive Status and Larval Development of Freshwater Mussels (Bivalvia: Unionida)07:22

Standardizing a Non-Lethal Method for Characterizing the Reproductive Status and Larval Development of Freshwater Mussels (Bivalvia: Unionida)

Published on: October 4, 2019

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)09:38

Updated Protocol for the Assembly and Use of the Minibioreactor Array (MBRA)

Published on: September 5, 2025

Related Concept Videos

Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...