Two different sets of data which hypothesis tests to use?

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In summary, the conversation discusses two different questions involving testing hypotheses for a statistics assignment. The first question involves flu shots and the most appropriate test is a chi squared test. The second question compares peak expiratory flow between two groups of children and the appropriate test depends on the type of data being compared. For comparing mean responses, a 2-sample z or t test should be used, while for comparing variability, ANOVA is the best option.
  • #1
FishBulb
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Hi there.

For a statistics assignment, I have two different questions involving testing hypotheses.

The first question concerns flu shots and whether or not patients had flu or no flu. So we have two categorical response variables(flu or no flu) and three categorical explanatory variables(no flu shot, one flu shot, two flu shots). I'm assuming use a chi squared test?

The second question involves peak expiratory flow between two different groups of children given two different medications. It asks to provide the most appropiate graph to compare the distribution, and also to carry out the appropriate hypothesis test. For comparing two different sets of data, both with a continuous response variable and a categorical explanatory variable, which would be the best hypothesis test?

Thanks in advance for any help.
 
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  • #2
That looks like an "ANOVA" (analysis of variance) problem to me.
 
  • #3
FishBulb said:
Hi there.

For a statistics assignment, I have two different questions involving testing hypotheses.

The first question concerns flu shots and whether or not patients had flu or no flu. So we have two categorical response variables(flu or no flu) and three categorical explanatory variables(no flu shot, one flu shot, two flu shots). I'm assuming use a chi squared test?

The second question involves peak expiratory flow between two different groups of children given two different medications. It asks to provide the most appropiate graph to compare the distribution, and also to carry out the appropriate hypothesis test. For comparing two different sets of data, both with a continuous response variable and a categorical explanatory variable, which would be the best hypothesis test?

Thanks in advance for any help.

the first one seems to be the correct test.

for the second, if you're comparing responses for two different medications, you should use a 2-sample z or t test, if you're comparing mean responses. if you're comparing the variability of the responses (standard deviations), then you should use ANOVA.
 

Related to Two different sets of data which hypothesis tests to use?

1. What is the difference between parametric and non-parametric tests?

Parametric tests assume that the data follows a specific distribution, such as a normal distribution, and require certain assumptions to be met. Non-parametric tests do not make any assumptions about the distribution of the data and are more flexible, but may have less power.

2. How do I determine which type of test to use?

The type of test to use depends on the nature of your data and the research question being asked. If your data is normally distributed and you have a specific hypothesis about the population mean, a parametric test such as a t-test may be appropriate. If your data is not normally distributed or you do not have a specific hypothesis, a non-parametric test such as the Wilcoxon rank-sum test may be more suitable.

3. Can I use a parametric test on non-normal data?

While parametric tests are more powerful, they may not give accurate results if the assumptions are not met. If your data is non-normal, it is recommended to use a non-parametric test to avoid potential errors.

4. What is the difference between a one-tailed and two-tailed test?

A one-tailed test looks for a difference in only one direction, whereas a two-tailed test looks for a difference in either direction. For example, a one-tailed test may be used to see if a new medication improves symptoms, while a two-tailed test may be used to see if there is any difference in symptoms between two groups.

5. How do I interpret the results of a hypothesis test?

The results of a hypothesis test will provide a p-value, which represents the probability of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is true. If the p-value is below the chosen significance level (usually 0.05), we reject the null hypothesis and conclude that there is a significant difference. If the p-value is above the significance level, we fail to reject the null hypothesis and conclude that there is not enough evidence to support a significant difference.

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