Question

Is this statement true? Why?

The level of significance and sample size shows that the hypothesis can be rejected. The p value in most cases would increase with the sample size. If the sample sizes becomes larger they are subjected to cause the hypothesis to reject. This happens in companies all the time in many different areas. For instance, lets say a company is running a huge marketing campaign. More than often certain companies or even small companies will only choose to do marketing on that type of scale if they know that their sales will increase. If the sample size increase this opens the door for the hypothesis (the thought of people buying into the company after marketing) to be rejected. I tried to look more into the book and understand this. If anyone has input I would love the constructive criticism.

Answer 1

In statistics, a sample refers to the observations drawn from a population. Sample size is used in market research and defines the number of subjects that should be included within a sample. Having the right sample size is crucial in finding a statistically significant result. The larger the sample size, the more reliable the results; however, larger sample size means more time and money

To determine the right sample size for market research we need to take care of following

1. Population Size — How many total people fit your demographic? For instance, if you want to know about mothers living in the US, your population size would be the total number of mothers living in the US. Not all populations sizes need to be this large. Even if your population size is small, just know who fits into your demographics. Don’t worry if you are unsure about this exact number. It is common for the population to be unknown or approximated between two educated guesses.
2. Margin of Error (Confidence Interval) — No sample will be perfect, so you must decide how much error to allow. The confidence interval determines how much higher or lower than the population mean you are willing to let your sample mean fall. If you’ve ever seen a political poll on the news, you’ve seen a confidence interval. For example, it will look something like this: “68% of voters said yes to Proposition Z, with a margin of error of +/- 5%.”
3. Confidence Level — How confident do you want to be that the actual mean falls within your confidence interval? The most common confidence intervals are 90% confident, 95% confident, and 99% confident.
4. Standard of Deviation — How much variance do you expect in your responses? Since we haven’t actually administered our survey yet, the safe decision is to use .5 – this is the most forgiving number and ensures that your sample will be large enough.

A larger sample size should hypothetically lead to more accurate or representative results, but when it comes to surveying large populations, bigger isn't always better. In fact, trying to collect results from a larger sample size can add costs – without significantly improving your results.