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What does a BF10 of 0.234 indicate in the context of the Bayesian one-sample t-test for height?

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Guia de Análise Bayesiana com JASP

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Um BF10 de 0,234 no contexto do teste t de uma amostra bayesiano indica que os dados fornecem evidências a favor da hipótese nula em relação à hipótese alternativa. Em outras palavras, isso sugere que é mais provável que a média da amostra seja igual à média da população do que que haja uma diferença significativa. Um BF10 menor que 1 geralmente indica que a evidência está a favor da hipótese nula.

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Bayes Factor

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Prior distribution

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Econometria

What does a BF10 of 0.234 indicate in the context of the Bayesian one-sample t-test for height?

Guia de Análise Bayesiana com JASP

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Guia de Análise Bayesiana com JASP

What are the three ways of describing central tendency?

Evaluates the conditional probability between two competing hypotheses. The aim is to quantify support levels for each hypothesis, which can be updated as new information becomes available, instead of generating definitive accept or reject hypothesis decisions.
Bayes Factor

A statistical distribution that can be used to capture the amount of (un)certainty in a population parameter. This distribution is then weighted by the sample data to obtain the posterior, which is used to make an inference.
Prior distribution

The odds of the outcome before the evidence is considered. These can be uninformative (assigning equal probabilities to all possibilities) or informative based on previous findings/knowledge.
Prior odds

Posterior odds = Bayes factor $\times$ prior odds. From this formula, we see that the Bayes' factor (BF) tells us whether the data provides evidence for or against the hypothesis assigns equal probabilities to all possibilities
Posterior odds

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Econometria

What does a BF10 of 0.234 indicate in the context of the Bayesian one-sample t-test for height?

Guia de Análise Bayesiana com JASP

Respostas

Ainda com dúvidas?

Essa pergunta também está no material:

Guia de Análise Bayesiana com JASP

What are the three ways of describing central tendency?

Evaluates the conditional probability between two competing hypotheses. The aim is to quantify support levels for each hypothesis, which can be updated as new information becomes available, instead of generating definitive accept or reject hypothesis decisions.Bayes Factor

A statistical distribution that can be used to capture the amount of (un)certainty in a population parameter. This distribution is then weighted by the sample data to obtain the posterior, which is used to make an inference.Prior distribution

The odds of the outcome before the evidence is considered. These can be uninformative (assigning equal probabilities to all possibilities) or informative based on previous findings/knowledge.Prior odds

Posterior odds = Bayes factor $\times$ prior odds. From this formula, we see that the Bayes' factor (BF) tells us whether the data provides evidence for or against the hypothesis assigns equal probabilities to all possibilitiesPosterior odds

How close does the sample reflect the whole population?

What does a BF01 of 4.279 indicate in the context of the Bayesian one-sample t-test for height?

What does a BF10 of 61312.288 indicate in the context of the Bayesian one-sample t-test for mass?

Mais conteúdos dessa disciplina

Evaluates the conditional probability between two competing hypotheses. The aim is to quantify support levels for each hypothesis, which can be updated as new information becomes available, instead of generating definitive accept or reject hypothesis decisions.
Bayes Factor

A statistical distribution that can be used to capture the amount of (un)certainty in a population parameter. This distribution is then weighted by the sample data to obtain the posterior, which is used to make an inference.
Prior distribution

The odds of the outcome before the evidence is considered. These can be uninformative (assigning equal probabilities to all possibilities) or informative based on previous findings/knowledge.
Prior odds

Posterior odds = Bayes factor $\times$ prior odds. From this formula, we see that the Bayes' factor (BF) tells us whether the data provides evidence for or against the hypothesis assigns equal probabilities to all possibilities
Posterior odds