Alpha Significance: Understanding & Analyzing Data Set

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SUMMARY

The discussion centers on the significance of the test statistic in hypothesis testing, specifically using the Z value in relation to the normal distribution. The calculated test statistic exceeding 1.96 indicates that the null hypothesis can be rejected, suggesting that the samples do not originate from a population with the specified mean and standard deviation. This conclusion is drawn while acknowledging a small risk of random chance, referred to as alpha. The process emphasizes the importance of understanding the relationship between the test statistic and the normal curve.

PREREQUISITES
  • Understanding of hypothesis testing and null hypothesis
  • Familiarity with Z scores and normal distribution
  • Knowledge of statistical significance and alpha levels
  • Ability to interpret test statistics in the context of data analysis
NEXT STEPS
  • Study the calculation and interpretation of Z scores in depth
  • Learn about different types of hypothesis tests, including t-tests and chi-square tests
  • Explore the implications of Type I and Type II errors in hypothesis testing
  • Investigate the use of statistical software like R or Python for hypothesis testing
USEFUL FOR

Students in statistics, data analysts, and researchers involved in hypothesis testing and data interpretation will benefit from this discussion.

gummz
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Homework Statement


What conclusions can be drawn from this data set? What assumptions are you making?

Homework Equations


http://i.imgur.com/M9YQGAF.png
I hope this is legible.

The Attempt at a Solution


The solution is what I'm having trouble with.
I just don't get how that test statistic has anything to do with whether that hypothesis is rejected or not, and what meaning it has for the test statistic to be larger than that number.
 
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This is an interesting question that might require a lengthy discussion.
In general, the process of calculating a test statistic (Z value) is designed so that you can relate your data set to the normal curve. Remember that most normally distributed stuff will be observed in the biggest part of the curve, and it is less common to see something in the tails (left or right extremes) of the curve.
When you find ##|Z_{0.025}| = 1.96##, that is giving you a value which says that 2.5% of observations from a population that has mean of zero and standard deviation 1 (the normal curve standard) will be greater that 1.96 and 2.5% will be less that -1.96. Those two tails account for 5% of the population.
Your calculated test statistic was greater than 1.96, which indicates that it would be an uncommon observation if your null hypothesis is true.
Your conclusion, then is that, accepting the small risk that your observation was a random chance (alpha), you can reject the null hypothesis. That is that these samples did not come from a population with the stated mean and standard deviation.
 

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