Test whether the diets are different from one another at ##α=5\%##

In summary, the objective is to determine if there are statistically significant differences between the diets by conducting a hypothesis test at a significance level of 5%.
  • #1
chwala
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Homework Statement
See attached
Relevant Equations
stats
Looking at stats today,

1709376624115.png

In my working i have;

Let

##H_0 = μ_1=μ_2##

v/s

##H_1 = μ_1-μ_2≠ 0##

then,

##\bar x = \dfrac{134+83+...+123}{12}=120##

##\bar y = \dfrac{70+118...+94}{7}=101##

##t=\dfrac{\bar x- \bar y}{S_p ⋅\sqrt {\dfrac{1}{n_1}+\dfrac{1}{n_2}}}##

##t=\dfrac{120-101}{21.21 \sqrt {\dfrac{1}{12}+\dfrac{1}{7}}}##

##t=1.89##

and,

##t_{[17, 0.05]} =2.11##

since ##t_{calculated} < t_{[17, 0.05]}## that is ##1.89 < 2.11## we accept the null hypothesis that the ration are not different from one another and reject the alternative hypothesis that the ration are different from one another.

your insight is welcome...cheers.
 
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  • #2
chwala said:
##t_{[17, 0.05]} =2.11##
Did you use a two-tailed test or a one-tailed test?

As a side note, whatever statistics textbook this is, the publisher didn't spend much money on copy edits.
"The following are the grains in weight..." -- gains in weight?
"Test whether the ration are differently ..."
The table headings are confusing, with both rows headed by "High Protein." The table rows could be more easily understood if the rows showed the two groups of mice.
 
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  • #3
Mark44 said:
Did you use a two-tailed test or a one-tailed test?

As a side note, whatever statistics textbook this is, the publisher didn't spend much money on copy edits.
"The following are the grains in weight..." -- gains in weight?
"Test whether the ration are differently ..."
The table headings are confusing, with both rows headed by "High Protein." The table rows could be more easily understood if the rows showed the two groups of mice.
Two tailed test.
 
  • #4
The formulas you show seem to be the right ones, but to verify your result I would need to do a fair amount of calculations that you don't show, such as the two sample means, the two sample variations (and standard deviations), and would need to check that you came up with the right value that corresponds to the t value you found.
 
  • #5
Mark44 said:
The formulas you show seem to be the right ones, but to verify your result I would need to do a fair amount of calculations that you don't show, such as the two sample means, the two sample variations (and standard deviations), and would need to check that you came up with the right value that corresponds to the t value you found.
That's the whole essence of insight, you're a mathematician...the sample mean is shown ...I know that you can calculate the pooled variance too... Verification is an exercise on your part...

Your part (insight ); would be to tell whether my substitutions are correctly done...and whether there are better alternatives...

...I am pretty sure you that you can check from the t distribution table... degree of freedom vs the given alpha level where I got the ##2.11## value. Cheers mate.
 
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  • #6
chwala said:
That's the whole essence of insight, you're a mathematician...the sample mean is shown
I know how to do this, and have done it many times in the past. I just don't feel like going through the motions to do so. What you could do is to double-check your calculations.
chwala said:
I know that you can calculate the pooled variance too... Verification is an exercise on your part...
My idea about the insights is to confirm that you have used the right formulas, but not re-do all your calculations.
chwala said:
Your part probably would be to tell whether my substitutions are correct.

...I am pretty sure you also can check from the t distribution table... degree of freedom vs the given alpha level where I got the 2.11#. cheers mate.
Same as above.
 
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FAQ: Test whether the diets are different from one another at ##α=5\%##

What does α=5% mean in the context of testing whether diets are different?

α=5% refers to the significance level of the test. It represents the probability of rejecting the null hypothesis when it is actually true. In other words, there is a 5% risk of concluding that the diets are different when they are not.

What statistical test should be used to compare the diets?

The choice of statistical test depends on the data characteristics. Common tests include ANOVA (Analysis of Variance) if comparing more than two diets with normally distributed data, or a Kruskal-Wallis test if the data does not meet normality assumptions. For comparing two diets, a t-test or Mann-Whitney U test may be appropriate.

What is the null hypothesis in this test?

The null hypothesis (H0) in this context is that there is no difference between the diets. This means that any observed differences in the data are due to random variation rather than a true effect of the diets.

How do you interpret the p-value obtained from the test?

If the p-value is less than or equal to 0.05 (the chosen α level), you reject the null hypothesis and conclude that there is a statistically significant difference between the diets. If the p-value is greater than 0.05, you fail to reject the null hypothesis, indicating that there is not enough evidence to suggest a difference between the diets.

What are the assumptions that need to be met for the test to be valid?

For an ANOVA, the assumptions include normality of the data, homogeneity of variances (equal variances among groups), and independence of observations. For non-parametric tests like Kruskal-Wallis, the assumptions are less stringent but still require independent observations and similar shapes of distributions. Violations of these assumptions may require data transformation or the use of different statistical tests.

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