Hypotheses
Understanding hypotheses is crucial for hypothesis testing, which is a foundation for making decisions in both business and scientific research. When you conduct a statistical test, you always start by stating two competing hypotheses: the null hypothesis and the alternative hypothesis.
The null hypothesis (often written as H0) is the default or status quo assumption. It typically suggests there is no effect, no difference, or no relationship between variables. For instance, in a business context, suppose you want to test if a new website design increases conversion rates. The null hypothesis would state: "The new design does not change conversion rates compared to the old design."
On the other hand, the alternative hypothesis (written as H1 or Ha) represents what you aim to support. It proposes that there is an effect, a difference, or a relationship. Continuing the previous example, the alternative hypothesis would be: "The new website design changes conversion rates compared to the old design." This could be a two-sided alternative (any change) or one-sided (an increase or decrease).
In scientific research, these hypotheses guide experiments and data collection. For example, if a pharmaceutical company tests a new drug, the null hypothesis might state: "The new drug has no effect on blood pressure," while the alternative hypothesis claims: "The new drug lowers blood pressure."
Setting up clear null and alternative hypotheses ensures your statistical tests are objective and your conclusions are valid.
Type I and Type II Errors:
A Type I error occurs when you reject the null hypothesis when it is actually true (a "false positive").
A Type II error happens when you fail to reject the null hypothesis when the alternative hypothesis is true (a "false negative").
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Can you give more examples of null and alternative hypotheses?
How do I decide whether to use a one-sided or two-sided alternative hypothesis?
What happens if the data supports the null hypothesis?
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Understanding hypotheses is crucial for hypothesis testing, which is a foundation for making decisions in both business and scientific research. When you conduct a statistical test, you always start by stating two competing hypotheses: the null hypothesis and the alternative hypothesis.
The null hypothesis (often written as H0) is the default or status quo assumption. It typically suggests there is no effect, no difference, or no relationship between variables. For instance, in a business context, suppose you want to test if a new website design increases conversion rates. The null hypothesis would state: "The new design does not change conversion rates compared to the old design."
On the other hand, the alternative hypothesis (written as H1 or Ha) represents what you aim to support. It proposes that there is an effect, a difference, or a relationship. Continuing the previous example, the alternative hypothesis would be: "The new website design changes conversion rates compared to the old design." This could be a two-sided alternative (any change) or one-sided (an increase or decrease).
In scientific research, these hypotheses guide experiments and data collection. For example, if a pharmaceutical company tests a new drug, the null hypothesis might state: "The new drug has no effect on blood pressure," while the alternative hypothesis claims: "The new drug lowers blood pressure."
Setting up clear null and alternative hypotheses ensures your statistical tests are objective and your conclusions are valid.
Type I and Type II Errors:
A Type I error occurs when you reject the null hypothesis when it is actually true (a "false positive").
A Type II error happens when you fail to reject the null hypothesis when the alternative hypothesis is true (a "false negative").
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