Independent t - test

#1
I am performing Independent sample t-test to compare the means of haemostasis time (primary endpoint) between independent categorical variables like gender, diabetic, non-diabetic. And also comparing to check if it is related to the outcome rebreeding / no-rebleeding. Total study population is 70.

I am using the software https://www.socscistatistics.com/tests/studentttest/default2.aspx

(1) Please help to know if I should select one-tailed or two-tailed?
(2) For 1 variable (diabetic and non-diabetic), occurrence of diabetes is only in 1 subject. How to calculate mean Haemostasis time for 1 subject?
 

staassis

Active Member
#2
The direction of your test mirrors the direction of your alternative hypothesis. If the alternative hypothesis is two-sided then the test is two-sided. If the alternative hypothesis is one-sided then the test is one-sided.

No population can be studied on only 1 observation.
 

Karabiner

TS Contributor
#4
The direction of your test mirrors the direction of your alternative hypothesis. If the alternative hypothesis is two-sided then the test is two-sided. If the alternative hypothesis is one-sided then the test is one-sided.
I beg to differ. Never heard of such a rule. Working hypotheses and statistical Null hypotheses
are not related this way. Otherwise one-tailed tests would be the rule. Usually, one-tailed tests
are performed if the scientist has absolutely no interest in results which go in the unexpected
direction. Which not often is the case in science (I suppose).

With kind regards

Karabiner
 

staassis

Active Member
#5
I beg to differ. Never heard of such a rule. Working hypotheses and statistical Null hypotheses
are not related this way. Otherwise one-tailed tests would be the rule. Usually, one-tailed tests
are performed if the scientist has absolutely no interest in results which go in the unexpected
direction. Which not often is the case in science (I suppose).

With kind regards

Karabiner
I never mentioned any "working hypotheses". I said: "alternative hypothesis". By definition, the alternative hypothesis (usually denoted with "H1" or "Ha") is one of the two formal, statistical hypotheses which are compared by the statistical test in question. The statistical test allows one to decide whether to accept the null hypothesis (H0) or reject the null hypothesis and accept the alternative hypothesis. For example, in the comparison

H0: mu_1 <= mu_2,
H1: mu_1 > mu_2,

the 1st row describes the null hypothesis and the 2nd row describes the alternative hypothesis. The null hypothesis is always the one with sign "=", "<=", ">=". By definition.... This is Statistics 101. Well, actually, it was called "Statistics 60" at my university.
 

Karabiner

TS Contributor
#6
Ah, now I see what this should mean. So the statistical (?) alternative hypothesis doesn't
directely relate to the working hypothesis.

With kind regards

Karabiner