- #1

Agent Smith

- 153

- 18

- TL;DR Summary
- An experiment to check for causality

We want to check whether specially treated bitter gourd is effective, marketed as

Group A: Are given

Group B: Are given untreated bitter gourd

Group C: Are not given either untreated bitter gourd or

A reference/baseline is collected: Blood sugar level

After 3 weeks, blood sugar levels are measured for all ##3## groups.

Response variable: Blood sugar level

Explanatory variable:

The Null Hypothesis ##H_o##: There is

The alternative hypothesis ##H_1##: There

Assume all conditions for a statistical experiment have been met adequately.

Possible outcomes:

1. No difference between B and C

2. Difference between B and C

3. No difference between A and B

4. Difference between A and B

My question is the number of groups being experimented upon. There are ##3## (A, B, C). Couldn't we achieve the same thing by using only ##2## groups (a control group B and a test/treatment group A)? How does having group C help? Are we trying to control for

I know that if outcome ##2## occurs, we can't rule out the placebo effect because group C knows they didn't get any treatment. Is this why we need group C, because if outcome ##2## happens, it (does it?) somehow lowers our confidence in outcome ##4##. My best guess is the outcome combination ##1## (does this mean the placebo effect is nonexistent or negligible?) and ##4## is best with respect to inferring causality (that

One of the main worries of modern medicine seems to be

*BitterHeal*, in lowering blood sugar levels in diabetics. They take a random sample of diabetics and assign them randomly to ##3## groups:Group A: Are given

*BitterHeal*Group B: Are given untreated bitter gourd

Group C: Are not given either untreated bitter gourd or

*BitterHeal*A reference/baseline is collected: Blood sugar level

*pre-experiment*of all participant diabetics in the experiment.After 3 weeks, blood sugar levels are measured for all ##3## groups.

Response variable: Blood sugar level

Explanatory variable:

*BitterHeal*The Null Hypothesis ##H_o##: There is

*no*(*statistically significant) difference*between the ##3## groups.The alternative hypothesis ##H_1##: There

*is*a statistically significant difference between the ##3## groups, i.e. blood sugar levels are lowered by*BitterHeal*.*Ab hinc*difference is equivalent to statistically significant difference.Assume all conditions for a statistical experiment have been met adequately.

Possible outcomes:

1. No difference between B and C

2. Difference between B and C

3. No difference between A and B

4. Difference between A and B

My question is the number of groups being experimented upon. There are ##3## (A, B, C). Couldn't we achieve the same thing by using only ##2## groups (a control group B and a test/treatment group A)? How does having group C help? Are we trying to control for

*the placebo effect*? I think not because both groups A and B receive bitter gourds (and they don't know whether it's treated/untreated gourds).I know that if outcome ##2## occurs, we can't rule out the placebo effect because group C knows they didn't get any treatment. Is this why we need group C, because if outcome ##2## happens, it (does it?) somehow lowers our confidence in outcome ##4##. My best guess is the outcome combination ##1## (does this mean the placebo effect is nonexistent or negligible?) and ##4## is best with respect to inferring causality (that

*BitterHeal*is antidiabetic).One of the main worries of modern medicine seems to be

*the placebo effect*(a psychosomatic phenomenon instead of a purely somatic/physical one)*,*because then the drug is a dud.