What is the difference between the null hypothesis and the research hypothesis?
Describe a specific example of each in your own words.
What types of hypothesis testing errors do you need to watch out for?
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The null hypothesis and the research hypothesis are two opposing statements in hypothesis testing.
The null hypothesis (H₀) states that there is no significant relationship or difference between variables under investigation. It assumes that any observed difference or relationship in data is due to chance or random factors. In other words, it represents the status quo or the default assumption until proven otherwise. Researchers usually try to disprove the null hypothesis.
For example:
Null Hypothesis (H₀): There is no significant difference in the average test scores between students who use study aids and those who do not.
On the other hand, the research hypothesis (H₁ or Ha) is the alternative hypothesis that predicts a relationship or difference between variables. It challenges the null hypothesis by proposing an alternative explanation or outcome. Researchers aim to provide evidence in support of the research hypothesis.
For example:
Research Hypothesis (H₁): Students who use study aids will have higher average test scores than those who do not.
In hypothesis testing, there are two types of errors:
1. Type I Error (α-error): This occurs when the null hypothesis is incorrectly rejected, meaning there is a claim of a significant effect or relationship when it does not exist. It is also referred to as a false positive. It suggests a rejection of the null hypothesis when there should not have been one.
2. Type II Error (β-error): This occurs when the null hypothesis is incorrectly accepted, meaning there is a failure to detect a significant effect or relationship that actually exists. It is also referred to as a false negative. It implies a failure to reject the null hypothesis when there should have been one.
Both types of errors can have consequences in research, depending on the context and the specific hypotheses tested. It is important for researchers to be cautious and consider the potential for these errors when interpreting the results of hypothesis testing.
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