I need help with a unique null hypothesis that I would then be able to use a t test statistical analysis,the same hypothetical situation taken in the t test hypothesis, and turn it into a null hypothesis using a one-way ANOVA analysis and a two-way ANOVA.

I understand null and alternative, but I am having a hard time following step by step and having it all come together and it being correct. This is what I did for my pre-exam and I got a 71%. I thought I had most of it correct, but I didn't. So what I need is a complete unique answer to the questions that are in the 1st paragraph.
Thank you! If possible could you please use details as much as possible so when I do take my real exam I will fully be able to do this on my own.

Sure, I can help you with understanding and creating a unique null hypothesis for t-test, one-way ANOVA, and two-way ANOVA.

1. T-Test Null Hypothesis:
To form a null hypothesis for a t-test, you need to focus on comparing the means of two independent groups. Let's say you have two groups, Group A and Group B. Here's an example of a null hypothesis for a t-test:

Null Hypothesis: There is no significant difference between the means of Group A and Group B.

To perform a t-test, you need to collect data from both groups and calculate the t-value. This t-value helps to determine if the difference between the two group means is statistically significant.

2. One-Way ANOVA Null Hypothesis:
A one-way ANOVA compares the means of three or more independent groups. Let's say you have three groups, Group A, Group B, and Group C. Here's an example of a null hypothesis for a one-way ANOVA:

Null Hypothesis: There is no significant difference among the means of Group A, Group B, and Group C.

To perform a one-way ANOVA, you need to collect data from all three groups and calculate the F-value. The F-value is used to determine if there is a significant difference between at least one pair of group means.

3. Two-Way ANOVA Null Hypothesis:
A two-way ANOVA compares the means of two independent variables (factors) simultaneously. Let's say you have two independent variables, Factor A (with two levels: A1 and A2) and Factor B (with three levels: B1, B2, and B3). Here's an example of a null hypothesis for a two-way ANOVA:

Null Hypothesis: There is no significant interaction between Factor A (A1 and A2) and Factor B (B1, B2, and B3) on the mean outcome.

To perform a two-way ANOVA, you need to collect data from all combinations of the two independent variables and calculate the F-value. The F-value helps to determine if there is a significant interaction between the two factors on the mean outcome.

Remember, these are just examples, and you should tailor the null hypotheses according to the specific research question you have. Also, make sure to pay attention to the number of groups or factors involved to form the correct null hypothesis for each test.