A) Z-test for the mean (one sample)

a. You have one sample.

b. Claiming the mean of the population is or isnâ€™t some value.

c. They tell you or some other reason for knowing the population sigma.

d. Step 5: a normal curve. Can be one or two tailed. Have Z critical.

B) T-test for the mean (one sample)

a. You have one sample

b. Claiming the mean of the population

c. Tell you standard deviation or give you raw data. You donâ€™t get the population sigma.

d. Assuming conditions are meant, then the population is normal, etc.

C) Binomial test for a single proportion

a. Ask for proportion or percentage.

b. Example 15 out of my 100 are something, or 15% of being this value etc.

D) Z-test for difference in population means, two independent samples

a. Are the means the same or not the same? Oneâ€™s greater or lesser?

b. Are they independent or dependent?

c. Need to know sigma.

E) T-test for difference in population means, two independent samples {Case 1 or Case 2}

a. When you donâ€™t get the population sigma.

b. Only given raw data or standard deviation.

c. Looking if the means are the same, oneâ€™s greater? Lesser? Etc.

d. Are these standard deviation difference because the population is the same? (Case 1)

i. Are the underlying variances the same?

e. True sigma of the population is difference from the other population. S1, S2. {Case 2}

f. To find which case youâ€™re doing for, you do a test for the variances to see if the two

samples have the same variances. If the p-value < 0.05, theyâ€™re about the same.

F) t-test for difference, two related samples

a. Take the differences of before and after column, and then find the difference in the

mean differences.

b. Donâ€™t have to worry about case 1 or case 2 business. Youâ€™re just testing one column.

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