Question

Share with your peers the null and alternative hypotheses for a decision that is relevant to your personal or professional life. Remember in hypothesis testing the "equals" part will be with the null hypothesis, so you can have less than or equal to, greater than or equal to, or just equal to when defining the null hypothesis. The alternative hypothesis will be, then, either greater than, less than, or not equal to in relation to the above null criteria. See below for how it looks symbolically for the three possible setups.

I. H₀: μ ≥ μ₀ H_a: μ < μ₀
II. H₀: μ ≤ μ₀ H_a: μ > μ₀
III. H₀: μ = μ₀ H_a: μ ≠ μ₀

Note that a hypothesis test needs to be set up to be testable, so be sure to have it presented in a manner where you are testing the μ₀ value. Additionally, identify the Type I and Type II errors that could occur with your decision‐making process

I work as an airport greeter.

Answer 1

For an airport greeter profile, following hypothesis tests may be relevant:

(1) What proportion of people working as airport greeter see it as a long-term career after the new government regulations for the airport greeter job.

H_O = p≤0.7   (0.7 is the hypothesized value of the proportion of people working as airport greeter who see their job as a long-term career. The hypothesized value may be based on historical data, a pilot study or expert opinion)

H_a = p>0.7

Type-I error: (Rejecting null hypothesis when in fact it is true)

It is concluded that there has been an increase in the number of airport greeters (p>0.7) who see their job as a long-term career after the new government regulations when in fact the new govt. regulations have not changed the perception of the airport greeters seeing their job as long-term career.

Type-II error: (Accepting null hypothesis when in fact it is false)

It is concluded that there has been no increase in the number of airport greeters (p>0.7) who see their job as a long-term career after the new government regulations when in fact the new govt. regulations have led to an increase in the no. of the airport greeters seeing their job as long-term career.

(2) A greeter service provider company claims that those availing its service saves an average of 25 minutes =for security and immigration clearance compared to those who do it themselves.

H_O = μ₁- μ₂ ≥25

H_a = μ₁- μ₂ <25

Where, μ₁=average time for security and immigration clearance for passengers availing the service of the greeter service company

And, μ₂=average time for security and immigration clearance for passengers doing themselves

Type-I error: (Rejecting null hypothesis when in fact it is true)

It is concluded that the average time saved for security and immigration clearance for passengers availing the service of the greeter Service company was less than 25 minutes when in fact it was not less than 25 mins.

Type-II error: (Accepting null hypothesis when in fact it is false)

It is concluded that the average time saved for security and immigration clearance for passengers availing the service of the greeter Service company was not less than 25 minutes when in fact it was less than 25 mins.

(3) The average amount spent per person availing airport greeter services in the year 2020 is lower than the $100 level reported 2 years back.i.e in 2018 (reduction may be due to a shift towards economy class travel).

H_O = μ≥100   

H_a = μ<100

Type-I error: (Rejecting null hypothesis when in fact it is true)

It is concluded that average amount spent per person availing airport greeter services in the year 2020 is lower than what it was 2 years back (<$100) when in fact the average amount spent per person availing airport greeter services now compared to the amount 2 years back has not decreased.

Type-II error: (Accepting null hypothesis when in fact it is false)

It is concluded that average amount spent per person availing airport greeter services in the year 2020 has not reduced from what it was 2 years back (<$100) when in fact it has reduced.