Parametric and non-parametric analyses commonly share the following:

Key Points

• Parametric and non-parametric analyses are two broad categories of statistical analyses used in research and data analysis. While they differ in their underlying assumptions and techniques, they share some common aspects. One common aspect is the use of inferential statistics, which involves making inferences or conclusions about a population based on sample data.

1- Testing of null hypotheses only:

• This option is not correct because both parametric and non-parametric analyses can involve testing null hypotheses. In hypothesis testing, researchers formulate a null hypothesis and an alternative hypothesis, and statistical tests are used to assess the evidence against the null hypothesis.

2. Chain of reasoning based on inferential statistics:

• This option is correct. Both parametric and non-parametric analyses follow a chain of reasoning based on inferential statistics. In both cases, researchers use statistical techniques to analyze the data, calculate test statistics, and make inferences about the population based on the sample data.

3. Statistics as means and frequencies:

• This option is not entirely accurate. While both parametric and non-parametric analyses involve using statistical measures such as means and frequencies, it is not something they commonly share. The choice of statistical measures used in the analysis depends on the nature of the data and the specific research question.

4. Ordinal and interval scale data:

• This option is not accurate in terms of what parametric and non-parametric analyses commonly share. Parametric analyses typically assume the data to follow a specific distribution and require interval or ratio scale data. Non-parametric analyses, on the other hand, do not rely on these assumptions and can be used with various types of data, including ordinal and interval scale data.

Hence option 2 is the correct answer.