Use a calculator tool from Module 10 to determine the p-value associated with the hypothesis test examining if there is a statistically significant difference between the proportion of people willing to take risks in the two groups. Round to 3 decimal places. While it may even be questionable that the research team reported a significant result at the 0.05 level based upon our findings in question 1, let’s observe the major impact a slight change makes in our calculation. Recalculate the p-value from part 1, but let’s pretend that 18 of the 22 participants in the high-power group took the gambling risk. This new p-value speaks to the volatility of results when low sample sizes are used. Round this p-value to 3 decimal places.
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
19 of 22 participants (86.36%) from the high-power posing group took a gambling risk to double their money, while 12 of 20 (60%) from the low-power posing group took the gambling risk. Use a calculator tool from Module 10 to determine the p-value associated with the hypothesis test examining if there is a statistically significant difference between the proportion of people willing to take risks in the two groups. Round to 3 decimal places.
While it may even be questionable that the research team reported a significant result at the 0.05 level based upon our findings in question 1, let’s observe the major impact a slight change makes in our calculation. Recalculate the p-value from part 1, but let’s pretend that 18 of the 22 participants in the high-power group took the gambling risk. This new p-value speaks to the volatility of results when low sample sizes are used. Round this p-value to 3 decimal places.
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