Let’s begin by discussing what hypothesis testing is. This will be the foundation for our discussion on consumer risk.
First, you need to make a statement about population parameters. This is the null hypothesis. It is denoted by Ho. Step 2 is to provide an alternative or alternate hypothesis, denoted with Ha. This will be the opposing hypothesis to the null hypothesis.
To determine whether you are statistically confident in rejecting the Ho, you use sample data. If you reject the Ho, the statistical conclusion will be that the alternate or alternate hypothesis H is true. There is a chance that you will make an error when rejecting or refusing the Ho. You, the researcher, decide the risk level you are willing to take for this error.
When deciding what to do with the Ho, you can make either one of these errors.
- Type 1 or the producer’s risk is also known as an alpha error. An alpha error occurs when you believe that something significant occurred, but it didn’t. You might reject a product that you believe is defective because it isn’t. This is known as a producer’s mistake because you rejected a unit you could have sold. Your risk of being wrong can be as low as 1%, 5%, or 10%.
- Type 2 or beta error, also known as the customer’s risk. This error is when you don’t reject the Ho when it should. You failed to act on something important. Your product was defective. You didn’t notice it and sent the wrong product to the customer. The consumer is now at risk of receiving a defective product and possibly causing harm. This level of risk is typically set at 80% to 90% or any other reasonable value.
The concept of power is closely associated with the consumer’s risk or Beta. If you can see something important, power is the ability to recognize it.
The formula is Power = 1 – Beta.
You will have more power to protect consumers’ rights by making it easier for them to reject the Ho when they should. Using larger sample sizes increases power. If the hypothesis test had sufficient power, the defect would have been detected and you wouldn’t have shipped it to the customer.
Three benefits to identifying the risk of the consumer
Your efforts to reduce the risk for your customer will be appreciated by them.
1. Your customer’s risk can be minimized, which will lower the likelihood of you sending a bad product.
Typ 2 errors can lead to you releasing criminals or releasing drugs that aren’t effective.
2. Power measures the risk of a consumer.
Many statistical software packages can calculate power. You can increase power and lower your consumer’s risk by taking larger samples.
3. Your customer is relieved of the burden
Producers must balance risk and cost. While increasing power costs more, it lowers the risk for your customer. While you work out how to reduce both, consider shifting the consumer risk to your customer-to-producer risk.
What is the importance of understanding consumer risk?
Hypothesis testing can reduce consumer risk. Hypothesis testing can help reduce the risks to both consumers and producers.
Reduce the risk to consumers
You can control the size and power of your samples to reduce the chances that your customer will get defective products.
Comparison of producer and consumer risk costs
Type 1 (producer’s risk) and Type 2, (consumer’s risk), work in opposing directions. It can be difficult to choose which one to protect.
Let’s take a look at the pharmaceutical industry. Are they protecting against the producer or consumer risk? They would have to pay the producer’s risk if they reject a batch of good pills. They would be responsible for the risk to both Walgreens and the patient if they shipped bad pills to Walgreens.
Shipping bad products to customers and consumers seem to be cheaper than rejecting good products.
Your customer should not be your inspection team
It is not a good way to build a long-term relationship with your customer if they find your product defective. Type 2 mistakes and consumer risk can be costly.
