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Regression and Hypothesis Testing
Student Name
University Name
Course Number: Course Title
Instructor’s Name
Assignment Due Date
, 2
Hypothesis Testing
The main aim of hypothesis testing is to determine whether or not to reject the null
hypothesis (there is no effect, there’s no difference) using a level of significance (say, α = 0.05) –
such that, when the p-value of the statistical test is less than 0.05, the null hypothesis is rejected,
otherwise, it is approved. I use hypothesis testing in my daily life. For instance, when Netflix
released the new Season of Money Heist I was a bit skeptical about watching it. I was not sure
whether it could live to the hype generates by the previous four seasons. Therefore, I put up a
poll on my Twitter account. The following were the hypotheses:
Ho: Money Heist Season 5 is not worth watching.
Ha: Money Heist Season 5 is worth watching.
After 24 hours, 74% of the respondents agreed that Money Heist Season 5 is a must
watch. Only 26% didn’t find it worth watching with the claim that it is overrated. Based on the
results, I rejected the null hypothesis and concluded that Money Heist Season 5 is worth
watching.
Based on the hypotheses, the population of interest was 18-35 years old males and
females (the age range of most of my followers). The sampling technique was simple random
sampling because the respondents randomly responded to the prompts. The following research
questions guided the responses:
H1: Guys, do you think Money Heist Season 5 is worth watching?
The hypothesis was a bit general and direct. There were no variables to be compared to
each other. The test statistic that I used to generate findings was frequency. The application made
it easier for me because at the end of the poll, it displayed the results.
Regression and Hypothesis Testing
Student Name
University Name
Course Number: Course Title
Instructor’s Name
Assignment Due Date
, 2
Hypothesis Testing
The main aim of hypothesis testing is to determine whether or not to reject the null
hypothesis (there is no effect, there’s no difference) using a level of significance (say, α = 0.05) –
such that, when the p-value of the statistical test is less than 0.05, the null hypothesis is rejected,
otherwise, it is approved. I use hypothesis testing in my daily life. For instance, when Netflix
released the new Season of Money Heist I was a bit skeptical about watching it. I was not sure
whether it could live to the hype generates by the previous four seasons. Therefore, I put up a
poll on my Twitter account. The following were the hypotheses:
Ho: Money Heist Season 5 is not worth watching.
Ha: Money Heist Season 5 is worth watching.
After 24 hours, 74% of the respondents agreed that Money Heist Season 5 is a must
watch. Only 26% didn’t find it worth watching with the claim that it is overrated. Based on the
results, I rejected the null hypothesis and concluded that Money Heist Season 5 is worth
watching.
Based on the hypotheses, the population of interest was 18-35 years old males and
females (the age range of most of my followers). The sampling technique was simple random
sampling because the respondents randomly responded to the prompts. The following research
questions guided the responses:
H1: Guys, do you think Money Heist Season 5 is worth watching?
The hypothesis was a bit general and direct. There were no variables to be compared to
each other. The test statistic that I used to generate findings was frequency. The application made
it easier for me because at the end of the poll, it displayed the results.
