Statistical Signifance with Multiple Sample Groups?

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SUMMARY

The discussion focuses on statistical significance in the context of multiple sample groups, specifically regarding pricing strategies for a product. The user conducted an experiment with 100 participants divided into 10 groups, each exposed to different price points ranging from $5 to $50. The null hypothesis posits that price does not influence purchasing decisions. A contingency table is suggested as an appropriate method to analyze the data and test the null hypothesis effectively.

PREREQUISITES
  • Understanding of null hypothesis and alternative hypothesis
  • Familiarity with contingency tables and chi-square tests
  • Basic knowledge of statistical significance and p-values
  • Experience with data collection and experimental design
NEXT STEPS
  • Research how to perform a chi-square test for independence
  • Learn about the assumptions and limitations of contingency tables
  • Explore methods for calculating p-values in statistical tests
  • Investigate alternative statistical tests for non-parametric data
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Statisticians, data analysts, researchers conducting experiments, and anyone interested in understanding the impact of pricing on consumer behavior.

Mbaboy
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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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You might try a contingency table.
 

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