One of the goals of marketing research is to

formulate and test hypotheses that are relevant for marketing decisions.

These questions may be, for example,

are people different from one another?

Is there willingness to pay for a product $305 or is it larger?

So hypothesis testing is a statistical procedure

that gets you to accept or reject a claim based on two statistical tests,

namely Z-Test and T-Test.

Similar to inferential analysis, it is important that

the sample is representative of the population of interest.

Specifically what you want to do is to

infer something from a larger group, the population.

However, you cannot survey everyone.

So you take a sample of this larger group.

To formulate hypotheses that amenable to testing,

we need to define what is called the null and

alternative hypothesis which are usually denoted H_sub_0 and H_sub_a respectively.

The alternative hypothesis is what we want to

test whereas the null hypothesis is the default assumption.

The null hypothesis is usually formulated as an equality about the quantity of interest

whereas alternative hypothesis can be formulated with a different sign or an inequality.

The objective of hypothesis testing is to see whether there is enough information in

the data to reject the null hypothesis and accept the alternative hypothesis.

Usually and ideally, hypotheses should

be formulated before you design the survey and collect the data.

And hypothesis testing should inform questionnaire and sample design.

Here I'm assuming that these hypotheses have been made already and that now,

you want to move forward and analyze the results to see if

there is enough support in the data to reject or accept your hypothesis.

So the first step is to formulate the hypotheses

based on what you think is true for the population.

Then, based on the statement,

based on the data, based on the scale used,

and based on the confidence level you want

to determine something called the sample statistic.

And you want to compare it to

the positive parameter and decide whether or

not the data support the original hypothesis.

Suppose that you want to test for means, means being equal,

means being different in the population compared to

the sample size or means being larger or smaller than a specific value.

Imagine that you have interval data on some means.

To make some hypotheses using your sample,

you can use one of two test: something called Z-Test and something called the T-Test.

They are about the same in the sense that they're designed to test hypotheses or means.

However it is important that we distinguish between the two of them.

Z-Test is appropriate when the samples,

where the population standard deviation is assumed to be known whereas

a T-Test is appropriate when both the

mean and the standard deviation of the populations are unknown.

So we'll focus on the Z-Test for simplicity.

The T-Test and the Z-Test,

however, are not too different from one another.