HYPOTHESIS TESTING
HYPOTHESIS TESTING
This module examines hypothesis testing as a key method in inferential statistics. It illustrates how researchers use sample data to evaluate claims about population means through systematic steps and decision-making rules. It also addresses potential errors in decision-making and explains the role of the alpha level in managing the risk of incorrect conclusions.
The Logic of Hypothesis
Researchers usually cannot observe every individual in a population because this is either impossible or impractical. Instead, they draw conclusions by collecting data from a representative sample and use this information to answer questions about the population.
Hypothesis testing is one of the most widely used techniques in inferential statistics. Although the specific details may differ across studies, the fundamental process of hypothesis testing remains consistent. This module introduces the general procedure for conducting a hypothesis test.
Hypothesis testing is a statistical procedure that uses sample data to assess the validity of a hypothesis about a population.
The fundamental logic of hypothesis testing follows these steps:
One. First, formulate a hypothesis about a population. The hypothesis typically centers on a specific population parameter. For example, a researcher may propose that Filipino adults gain an average of u equals seven pounds during the holidays.
Two. Next, before collecting a sample, the researcher uses the hypothesis to predict the expected values of the sample mean if the hypothesis is true. For example, if the population mean is u equals seven pounds, the sample mean should be approximately seven pounds. Some deviation is expected, since a sample rarely represents the population perfectly.
Three. Then, the researcher selects a random sample from the population. For example, the researcher may measure the weight change of a sample of n equals two hundred adults during the holiday period.
Four. Finally, the researcher compares the sample data with the prediction based on the hypothesis. If the sample mean matches the prediction, the hypothesis appears reasonable. If the difference is large, the hypothesis is likely incorrect.
Researchers usually apply hypothesis testing after completing a research study. The specific steps of the test depend on the research design and the type of data. Later chapters describe different forms of hypothesis testing for different research situations. This chapter focuses on the basic elements common to all hypothesis tests. To do this, it examines the simplest case, which uses a sample mean to test a hypothesis about a population mean.
The Four Steps of a Hypothesis Test
The Four Steps of a Hypothesis Test
Before the treatment, the original population had a mean tip of u equals sixteen percent. After the treatment, the population mean is unknown. Researchers do not know what happens to the average tip when waitresses wear red for all male customers. However, researchers have a sample of n equals thirty-six customers who were served by waitresses wearing red. This sample allows researchers to draw conclusions about the unknown population. Hypothesis testing provides a step-by-step method for using sample data to answer questions about a population.