What Is Hypothesis Testing?

Hypothesis testing is a method of testing an assumption regarding a population by exam🌳ining data from a sample. The process is sometimes called significance💃 testing,

Hypothesis testing begins when a researcher observes a pattern in a set of data. The𝕴 test determines whether the pattern can be exp🃏lained by coincidence or other variables.

The methodology used depends on the nature of the data and the reason for the analy💃sis.

How Hypothesis Testing Works

In hypothesis testing, an analyst tests a statistical sample, intending to provide evidence on the plausibility of the null hypothesis. Statistical analysts measure and examine a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses: the 澳洲幸运5官方开奖结果体彩网:null hypothesis and the alternative hypothesis.

The null hypothesis is usually a hypothesis of equality between population parameters; e.g., a null hypothesis may state that the population mean return is equal to zero. The alternative hypothesis is effectively the opposite of a null hypothesis. Thus, they are 澳洲幸运5官方开奖结果体彩网:mutually exclusive, and only one can be true. However, one of the two hypotheses will alw♈ays be true.

4-Step Process

澳洲幸运5官方开奖结果体彩网: The s🌳teps in hypothesis testing are:

1. State the hypotheses.
2. Formulate an analysis plan, which outlines how the data will be evaluated.
3. Carry out the plan and analyze the sample data.
4. Interpret the results and either reject the null hypothesis, or state that the null hypothesis is plausible, given the data.

Example of Hypothesis Testing

If a researcher wants to test if a penny is fair, the null hypothesis would state that the probability of landing on heads is exactly 50%. The alternative h🍰ypothesis would assert that the probability of landing on heads is not 50%. Mathematically, these would be represented as follows:

• Null hypothesis (Ho): P = 0.5
• Alternate hypothesis (Ha): P≠0.5

A random sample of 100 coin flips is taken, and the results are reco🙈rded.ꦗ If it is found that the outcomes were 40 heads and 60 tails, the analyst might infer that the penny does not have a 50% chance of landing on heads.

Further statistical testing, such as p-testing, might be performed to determine the likelihood of a fair coin landing heads🌠 up 40 times out of 100. If that likelihood is very ꧅low, the researcher would reject the null hypothesis and accept the alternative hypothesis.

If there were 48 heads and 52 tails, then it is plausible that the coin could be fair and still produce such a result. When the null hypothesis is "accepted," the analyst states that the difference between the expected results (50 heads and 50 tails) and the observed results (48 heads and 52 tails) is "explainable by chance alone."

Explain Like I'm Five

When scientists and researchers notice patterns in data, they need a way to make sure those patterns cannot be explained by coincidence or other variables. Hypothesis testing is a way of cꦅomparing different theories to find the best explanation for a pattern.

To perform hypothesis testing, the researcher lists different hypotheses about the re꧅lationships between the data they are looking at. Then they make a series of observations. The theory that does the best job of explaining their observa🧸tions is considered the strongest hypothesis.

The Bottom Line

Hypothesis testing is a statistical process that helps researchers determine the strength of a hypothesis. By using a well-formulated hypothesis and set of statistical tests, individuals or businesses can make inferences about the ⛎population that they are studying and draw conclusions based on the data presented.

All hypothesis tes🧸ting methods have the same four-step process, which includes stating the hypotheses, formulating an analysis plan, analyzing the sample data, and interpreting the results.