Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.9K
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...
2.9K
Reduced Mass Coordinates: Isolated Two-body Problem01:12

Reduced Mass Coordinates: Isolated Two-body Problem

1.8K
In classical mechanics, the two-body problem is one of the fundamental problems describing the motion of two interacting bodies under gravity or any other central force. When considering the motion of two bodies, one of the most important concepts is the reduced mass coordinates, a quantity that allows the two-body problem to be solved like a single-body problem. In these circumstances, it is assumed that a single body with reduced mass revolves around another body fixed in a position with an...
1.8K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.5K
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...
3.5K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

3.8K
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...
3.8K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

262
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
262
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.4K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaporitic Preservation of Modern Carotenoid Biomarkers and Halophilic Microorganisms in Mars Analog Hypersaline Environments.

Astrobiology·2025
Same author

Modeling of recovery efficiency of sampling devices used in planetary protection bioburden estimation.

Applied and environmental microbiology·2023
Same author

Multi-faceted metagenomic analysis of spacecraft associated surfaces reveal planetary protection relevant microbial composition.

PloS one·2023
Same author

Draft Genome Sequences of Spacecraft-Associated Microbes Isolated from Six NASA Missions.

Microbiology resource announcements·2023
Same author

Draft Genome Sequences of Fungi Isolated from Mars 2020 Spacecraft Assembly Facilities.

Microbiology resource announcements·2022
Same author

Performance of Multiple Metagenomics Pipelines in Understanding Microbial Diversity of a Low-Biomass Spacecraft Assembly Facility.

Frontiers in microbiology·2021

Related Experiment Video

Updated: Oct 27, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.8K

Application of a Bayesian Statistical Framework for Planetary Protection as a Means of Verifying Low-Biomass,

Andrei Gribok1, Arman Seuylemezian2, James Benardini2

  • 1Idaho National Laboratory, 2525 N. Fremont Ave., Idaho Falls, ID 83415, USA.

Life Sciences in Space Research
|July 20, 2021
PubMed
Summary

Bayesian statistics offer a new framework for assessing spacecraft contamination, improving planetary protection for Mars missions. This method provides a more accurate biological load assessment than traditional worst-case scenarios.

Keywords:
Bayesian inferenceBioburdenNASAPlanetary protectionprior distribution

More Related Videos

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.7K
Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass
07:44

Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass

Published on: July 14, 2023

1.4K

Related Experiment Videos

Last Updated: Oct 27, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.8K
Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.7K
Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass
07:44

Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass

Published on: July 14, 2023

1.4K

Area of Science:

  • Planetary Science
  • Microbiology
  • Statistics

Background:

  • Planetary protection protocols are crucial for missions to Mars to prevent biological contamination.
  • Current methods for tracking spacecraft bioburden rely on conservative worst-case scenarios, which can be inefficient.
  • Increasing mission complexity necessitates advanced statistical approaches for accurate contamination assessment.

Purpose of the Study:

  • To evaluate the efficacy of Bayesian statistics for assessing spacecraft contamination data.
  • To develop a statistically sound framework for planetary protection requirements.
  • To compare Bayesian methods with existing bioburden assessment approaches.

Main Methods:

  • Utilized components from the InSight mission as a test case.
  • Applied Bayesian statistical methods to planetary protection data sets, including zero-inflated data.
  • Analyzed various components based on surface area, biological count, and sampling techniques.
  • Evaluated different prior distributions, including non-informative priors.

Main Results:

  • Demonstrated a viable framework for applying Bayesian statistics to planetary protection.
  • Showcased the potential for more accurate bioburden assessment compared to heritage methods.
  • The Bayesian approach effectively addresses the distribution of spacecraft contamination.

Conclusions:

  • Bayesian statistics provide a robust and adaptable framework for planetary protection requirements assessment.
  • This approach can lead to more precise and efficient management of spacecraft bioburden.
  • Further development and utilization of this Bayesian framework are recommended for future space missions.