Help to Conduct Hypothesis Testing
What is Hypothesis Testing?
Hypothesis testing is a statistical analysis technique that compares two mutually exclusive theories about a population's parameter using sample statistics. The null and alternate hypotheses are the theories tested to see which one supports the data.
There are assumptions made during hypothesis testing, which include, the level of measurement, sampling method, the shape of the distribution, and sample size. The assumptions depend on the type of data provided.
Making assumptions, stating the null and alternate hypotheses, selecting sample distribution, selecting and computing test statistics, making decisions, and interpretation of the results are the steps followed in hypothesis testing. Sample size, p-value, confidence interval, sampling method, and level of measurement are the factors that should be considered in hypothesis testing.
Whereas it is dependent on factors such as sample size, test statistics confidence interval, and p-value, hypothesis testing compares two theories in data to help make statistical decisions, inferences, and conclusions.
Null and Alternative Hypothesis
There are two mutually exclusive theories in hypothesis testing, null and alternate hypotheses.
The null hypothesis (Ho) is a theory of no difference, meaning the effect size is equal to zero.
The alternate hypothesis (H1) states there is a significant difference. Alternate hypothesis tests the population parameters through not equal to, greater than, and less than statements.
Assumptions Made in Hypothesis Testing
Hypothesis testing assumptions provide a good fit for the results, minimizing the margin of error and preventing false conclusions. The assumptions made are the level of measurement, sampling procedure, distribution shape, and sample size. A random sampling method is assumed in 90% of statistical tests.
Steps in Hypothesis Testing
(1) Formulation is the first step in hypothesis testing depending on the data under study.
(2) Stating the null hypothesis and alternate hypothesis and the level of significance.
(3) Selecting the sampling distribution such as normal distribution and test statistics (t or z statistics).
(4) Depending on the test statistics chosen, compute the statistic.
(5) Making a decision based on the results obtained that should be consistent with the stated hypothesis. The decision may either reject or accept the null hypothesis.
The findings are interpreted to see whether there are significant differences present and the magnitude of the effect on the study.
Factors to Consider in Hypothesis Testing
The sample size is inversely proportional to the effect size. Effect size is the significant difference between the population parameter and the null hypothesis value.
A large sample size results in smaller effect size. The strength of the sample evidence against the null hypothesis is indicated by p-values. The findings are statistically significant if the p-value is less than the significance level hence reject the null hypothesis.
If the p-value is greater than the confidence level then we accept the null hypothesis. P-value can be a measure of how strong is the sample size used in the data. The confidence interval provides a range under which the values will lie.
A large confidence interval decreases the margin of error. For example, a 99% confidence interval has a 1% significant threshold, the margin of error compared to 95% which has a 5% margin of error. The sampling method determines the biases in the data used. Random sampling is mainly used in the statistical test.
Summary
Hypothesis testing is a crucial step in statistical analysis as it helps draw a conclusion on the significant difference between the groups or treatments used. Hypothesis testing involves a comparison of two theories concerning the data and the research.
The null hypothesis is a theory of no difference while alternate indicates the presence of a difference that is equal, less than, or greater than the parameter value.
Assumptions such as the level of measurement of variable, sampling method, the shape of the population distribution, and the sample size should be made depending on the type of data used.
A well-structured step for conducting the hypothesis testing includes making assumptions, stating the null and alternate hypothesis, selecting the sampling distribution, selecting and computing test statistics, and decision making.