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Related Concept Videos

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...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
What is a Hypothesis?01:14

What is a Hypothesis?

A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague statement. It...
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)...
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...

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Related Experiment Video

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A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

Hypothesis testing in animal social networks.

Darren P Croft1, Joah R Madden, Daniel W Franks

  • 1Centre for Research in Animal Behaviour, College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4QG, UK. d.p.croft@exeter.ac.uk

Trends in Ecology & Evolution
|July 1, 2011
PubMed
Summary

Behavioural ecologists use social network analysis to study animal social organization. This review clarifies statistical challenges, particularly data non-independence, and offers randomization-based null models for accurate hypothesis testing in animal social networks.

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Area of Science:

  • Behavioral Ecology
  • Animal Social Networks
  • Statistical Analysis

Background:

  • Social network analysis is a growing tool in behavioral ecology for understanding animal social structures.
  • Traditional statistical methods often fail with social network data due to inherent non-independence.
  • There is a recognized need for clearer guidance on analyzing animal social network data.

Purpose of the Study:

  • To review key considerations for analyzing animal social networks.
  • To address confusion regarding statistical pitfalls in hypothesis testing with network data.
  • To provide a practical guide for robust statistical analysis.

Main Methods:

  • Review of current practices and statistical challenges in animal social network analysis.
  • Explanation of the non-independent nature of social network data.
  • Introduction to null models based on randomization techniques.

Main Results:

  • Identified significant statistical challenges in analyzing animal social network data.
  • Highlighted the violation of assumptions in common statistical approaches due to data non-independence.
  • Demonstrated the utility of randomization-based null models for controlling structure and non-independence.

Conclusions:

  • Accurate statistical analysis of animal social networks requires careful consideration of data structure.
  • Randomization-based null models are essential for reliable hypothesis testing in this field.
  • This work provides a practical framework for behavioral ecologists analyzing animal social networks.