True/false Qs: for all 2-sample tests, sizes much be equal;

This was taken from 4 different test. Im trying to study for final and cannot figure out these questions. I am dumb. Sorry if the questions are easy or hard

1) True or False: For all two-sample tests, the sample sizes must be equal in the 2 groups.

2) True or False: In testing the difference between two proportions using the normal distribution, we may use a two-tailed Z test.

3) True or False: In testing a hypothesis, statements for the null and alternative hypotheses as well as the selection of the level of significance should precede the collection and examination of the data.

4) True or False: The larger is the p-value, the more likely one is to reject the null hypothesis.

5) True or False: The analysis of variance (ANOVA) tests hypotheses about the population variance.

6) True or False: For a given level of significance, if the sample size is increased, the probability of committing a Type I error will increase.

7) Which of the following would be an appropriate null hypothesis?

A) The population proportion is less than 0.65.
B) The sample proportion is less than 0.65.
C) The population proportion is no less than 0.65.
D) The sample proportion is no less than 0.65.

8) Interaction in an experimental design can be tested in

A) a completely randomized model.
B) a randomized block model.
C) a two-factor model.
D) all ANOVA models.

Re: True/false questions... Plz help with some **PLZ HELP ASAP

i would argue the first three to be:

False,
Maybe, and
Sort of.

This does nto bode well for the rest of the exam.

It is hoped that you discussed these issues very carefully in a classroom setting or they were very clearly spelled out in your text.

Re: True/false questions... Plz help with some **PLZ HELP ASAP

You aren't dumb. If you were dumb, you would not have been permitted to enroll in this course.

Although these are T/F questions, they can't be answered correctly without a good understanding of what they are on about.

I'll give some pointers below....

buddytohelpu said:

True or False: For all two-sample tests, the sample sizes must be equal in the 2 groups.

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Look up two-sample tests with "paired" samples, and with "independent" samples. Find something that elaborates on what they have in common, and what's different about them. After you get the answer to this question, try also to understand - when shoudl each type of two-sample test be used??

buddytohelpu said:

True or False: In testing the difference between two proportions using the normal distribution, we may use a two-tailed Z test.

Click to expand...

Whether we use two tailed or one-tailed tests depends on what our hypotheses are. Do we just want to test "Are x and y different?" then we use a two-tailed test. Eg "Do seatbelts make a difference to fatality rates?" On the other hand, if we want to test "Is x less than y?" eg "Do seatbelts reduce fatality rates?" we use a one-tailed test. This is not specific to testing proportions.

buddytohelpu said:

True or False: In testing a hypothesis, statements for the null and alternative hypotheses as well as the selection of the level of significance should precede the collection and examination of the data.

Click to expand...

This is a question about experimental design. I'll leave this to someone better qualified than I.

buddytohelpu said:

True or False: The larger is the p-value, the more likely one is to reject the null hypothesis.

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Think about what you do when you perform a test. You choose a significance level, then calculate a p-value, then compare, and make a decision..... if the p-value were larger, what kind of decision would you be more likely to make?

buddytohelpu said:

True or False: The analysis of variance (ANOVA) tests hypotheses about the population variance.

Click to expand...

Check the wikipedia article...

buddytohelpu said:

True or False: For a given level of significance, if the sample size is increased, the probability of committing a Type I error will increase.

Click to expand...

The p-value is the probability, if the null hypothesis is correct, of observing data as extreme as what we see. A significance level is a probability. Let's say we make it 1%, or 0.01. If assuming H[sub:2t1ww64w]0[/sub:2t1ww64w] means that our data is less likely than 1 in 100 (ie, our p-value was less than 0.01), we are faced with a choice - either reject the null hypothesis, or accept that amazing coincidences happen - a 1 in 100 chance occurred.

But what if H0 really is true? Well, out of 100 experiments at the 1% level, we'll accept in 99 times on average, and reject it once.

Now... what was your question again? Oh, yes. Look up 'Type I error' on wikipedia.

buddytohelpu said:

Which of the following would be an appropriate null hypothesis?

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Hypotheses are always about the population. After all, you can measure the sample - you don't need to hypothesize. Also, the null hypothesis should include an 'equals'.

buddytohelpu said:

Interaction in an experimental design can be tested in

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I suggest reading up on the topics of the four answers, and seeing what each one is for. A good text will start one of the sections with "YADA can be used to test interaction between variables ..."

Hope that helps!