Statistical Signifance with Multiple Sample Groups?

In summary, the speaker presents a simplified version of a problem where 100 people were split into 10 groups and offered a product at different prices. The null hypothesis is that price does not influence consumer choice. The speaker suggests using a contingency table to test this hypothesis.
  • #1
Mbaboy
19
0

Homework Statement


I will present a vary simplified version of the problem I am trying to model. Essentially I took 100 people and split them into 10 groups. To each group I tried to sell them a product. To the first group I priced the product at $5, to the second I priced it at $15, to the third $25, etc.

My null hypothesis is that the pricing of the product doesn't not influence the consumers choice to buy or not buy. My data looks something like this:

at $5 3 people bought
at $15 5 people bought
at $25 3 people bought
etc.

Again this is not my actual data, but the fundamentals are the same. What is an appropriate test I can use to show that I cannot reject the null hypothesis?
 
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  • #2
You might try a contingency table.
 

Related to Statistical Signifance with Multiple Sample Groups?

What is statistical significance with multiple sample groups?

Statistical significance with multiple sample groups is a statistical technique used to determine if there are any meaningful differences between two or more groups in a study. It allows researchers to make conclusions about the relationship between the variables being studied and to determine if these relationships are due to chance or if they are statistically significant.

How is statistical significance with multiple sample groups calculated?

Statistical significance with multiple sample groups is typically calculated using a statistical test, such as ANOVA (analysis of variance) or t-test. These tests compare the means of the groups and determine if the differences between them are statistically significant. The results of these tests are typically reported as a p-value, which indicates the probability of obtaining the observed results by chance.

What does a p-value in statistical significance with multiple sample groups mean?

A p-value in statistical significance with multiple sample groups is a measure of the probability of obtaining the observed results by chance. A p-value of less than 0.05 is typically considered statistically significant, which means that there is a 5% or less chance that the observed results are due to chance. This suggests that there is a meaningful difference between the groups being compared.

What are the limitations of statistical significance with multiple sample groups?

One limitation of statistical significance with multiple sample groups is that it does not necessarily indicate the practical significance or real-world importance of the differences between groups. Additionally, it is important to consider the sample size and the effect size (the magnitude of the differences between groups) when interpreting the results. Other factors, such as outliers or non-normal distributions, can also impact the validity of the results.

How can statistical significance with multiple sample groups be used in research?

Statistical significance with multiple sample groups can be used to help researchers make conclusions about the relationships between variables in a study. It can also be used to compare the effectiveness of different treatments or interventions, and to identify potential areas for further research. However, it is important to consider the limitations and potential biases when using statistical significance in research.

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