Understanding the null hypothesis

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In summary, the null hypothesis is a hypothesis that suggests an absence of difference, association or effect, the negation of which provides evidence for presence of difference, association or effect.
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Tyto alba
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I was reading Bio-statistics principles and practice by Antonisamy and stumbled upon the following:

Null hypothesis is a hypothesis that suggests an absence of difference, association or effect, the negation of which provides evidence for presence of difference, association or effect.

The only problem I'm facing with this definition is that it doesn't say-
  • differences between what,
  • association between what and
  • effect of what on what?
From random sources I know that a null hypothesis involve hypothesising that a theoretical distribution is consistent with an observed distribution, i.e. there is no difference. Also it can assume that there's no difference between the scores of two variables.

But I don't clearly understand what the book meant by association and effect?

I actually found this post in a different community where the OP is trying to establish a relationship (association) between driving speed and gender. Does the definition refer to such associations?
 
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SanjuktaGhosh said:
Null hypothesis is a hypothesis that suggests an absence of difference, association or effect, the negation of which provides evidence for presence of difference, association or effect.

The only problem I'm facing with this definition is that it doesn't say-
  • differences between what,
  • association between what and
  • effect of what on what?
Differences in this context refers to differences between the probability distributions of two or more random variables. Since the random variables are usually modeled as belonging to some parameterized probability distribution, this is equivalent to looking for differences in the parameters.

Association or effect just means some function that relates one random variable to another (or the parameters of one to the parameters of another). I don't think there is a real distinction between the two. Maybe "effect" is more associated with continuous random variables and "association" with categorical ones.
 
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Dale said:
Differences in this context refers to differences between the probability distributions of two or more random variables. Since the random variables are usually modeled as belonging to some parameterized probability distribution, this is equivalent to looking for differences in the parameters.

Association or effect just means some function that relates one random variable to another (or the parameters of one to the parameters of another). I don't think there is a real distinction between the two. Maybe "effect" is more associated with continuous random variables and "association" with categorical ones.

Oh thank you, that was a huge help.
 
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1. What is the null hypothesis?

The null hypothesis is a statement that assumes there is no relationship between two variables in a study. It serves as the default position that is tested against an alternative hypothesis.

2. Why is understanding the null hypothesis important?

Understanding the null hypothesis is important because it allows researchers to determine the significance of their findings. By testing the null hypothesis, researchers can determine if there is a meaningful relationship between variables or if their results are due to chance.

3. How is the null hypothesis determined?

The null hypothesis is determined based on the research question being investigated. It is typically formulated as a statement that the researcher believes to be true, and then it is tested against an alternative hypothesis that suggests there is a relationship between variables.

4. What happens if the null hypothesis is rejected?

If the null hypothesis is rejected, it means that the data collected provides enough evidence to support the alternative hypothesis. This suggests that there is a significant relationship between the variables being studied.

5. Is rejecting the null hypothesis always the goal?

No, rejecting the null hypothesis is not always the goal. In some cases, researchers may fail to reject the null hypothesis, indicating that there is no significant relationship between variables. This can also provide valuable information for further research and understanding of the topic.

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