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Last updated on 2024年6月8日

How do you choose the appropriate statistical test for your data?

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Choosing the right statistical test for your data can be a daunting task, but it's essential for accurate analysis and interpretation. Whether you're a researcher, student, or data analyst, understanding the nature of your data and the questions you're aiming to answer is the first step in this process. The type of data you have (categorical, ordinal, interval, or ratio), the distribution of your data (normal or non-normal), and the number of groups or variables you're comparing all play a crucial role in test selection. In this article, you'll discover how to navigate these considerations and select the most appropriate statistical test for your data analysis needs.

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Rishabh Mehta

Data Scientist at Dunnhumby | Azure DP-100 Certified | Ex-Wipro, IQVIA

1 Data Type

Before diving into statistical tests, it's important to classify your data. There are four main types: categorical (names or labels), ordinal (ordered categories), interval (numeric scales without a true zero), and ratio (numeric scales with a true zero). Categorical data often calls for chi-square tests, while ordinal data may require non-parametric tests like the Mann-Whitney U test. For interval and ratio data, parametric tests like the t-test or ANOVA are typically used if they meet certain assumptions, such as normality.

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Rishabh Mehta

Data Scientist at Dunnhumby | Azure DP-100 Certified | Ex-Wipro, IQVIA
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A statistical test for any data is chosen by the type of data to deal with: Qualitative Data : Qualitative Data majorly chi-square test helps in this Quantitative Data : This has 3 sub-part - Ordinal Data considered as Non-parametric data tests like man-whitney, u-test etc. are used in this case - nominal - intervals for both of these types of data can be tested by t-test, ANOVA etc.

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2 Test Purpose

Understanding the purpose of your test is crucial. Are you comparing means, proportions, or correlation between variables? If you're comparing means, a t-test (for two groups) or an ANOVA (for more than two groups) might be appropriate for normally distributed data. For proportions, chi-square tests are commonly used. When assessing the relationship between two continuous variables, Pearson's correlation coefficient is a good choice if the data is normally distributed; otherwise, Spearman's rank correlation is an alternative.

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Rishabh Mehta

Data Scientist at Dunnhumby | Azure DP-100 Certified | Ex-Wipro, IQVIA
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There are a variety of statistical tests serving different requirements, some of them majorly are explained below: 1) Test for change in mean of 2 samples - T-Test(sample size < 30), Normal -Z test ( sample size> 30) 2) Test for change in mean of >2 samples - ANOVA 3) Test sample follows distribution/ change is expected and observed data - chi-square 4) Test if data follows normal distribution - Kolmogorov- smirnov, Shapiro -wilk test 5) Test difference in 2 samples follows similar distribution - Cohen's D test

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3 Sample Size

Sample size affects your test choice. Small samples often don't meet the assumptions required for parametric tests, leading to the use of non-parametric alternatives. For example, instead of a t-test, you might use a Mann-Whitney U test for comparing two independent groups with small sample sizes. Larger samples can provide more flexibility and might allow you to use parametric tests even if the data slightly deviates from normality due to the central limit theorem.

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Rishabh Mehta

Data Scientist at Dunnhumby | Azure DP-100 Certified | Ex-Wipro, IQVIA
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Sample size has 2 significances for statistical tests A) Different test as per sample size - (I) While test difference in mean of 2 samples for a sample below 30 observations t-test is considered while in case of above 30 normal distribution is considered. (II) A smaller sample might not meet the assumption of a parametric test hence, non-parametric test like Man-whitney, u-test would be used. B) Defining degrees of freedom (df) - In case of, chi-square and F-test df would help you to calculate probability value.

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4 Assumption Checks

Before selecting a statistical test, check the assumptions. Parametric tests assume normal distribution and homogeneity of variances. Use plots and statistical tests like Shapiro-Wilk for normality and Levene's test for equal variances to verify these conditions. If assumptions are not met, consider transforming the data or using a non-parametric equivalent, which doesn't assume normality or homogeneity, such as the Kruskal-Wallis H test instead of an ANOVA.

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5 Data Pairing

Consider whether your data is independent or paired. Independent samples don't influence each other, while paired samples are related (e.g., before-and-after measurements). Independent samples might require an independent t-test or a chi-square test, while paired samples could use a paired t-test or Wilcoxon signed-rank test. The nature of your data pairing will significantly influence which statistical test is suitable for your analysis.

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6 Software Tools

Finally, use statistical software wisely to choose and perform your test. Tools like R , Python , SPSS , or Excel can run various tests and check assumptions. However, it's your responsibility to ensure the chosen test aligns with your data characteristics and research questions. Software can provide guidance and perform calculations, but it cannot replace your judgment and understanding of statistical principles.

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Rishabh Mehta

Data Scientist at Dunnhumby | Azure DP-100 Certified | Ex-Wipro, IQVIA
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Open source software helping in statistical tests - R, Python different libraries would help. Other software - Excel, SPSS, SAS, STATA are different software tools helping in statistical tests.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you choose the appropriate statistical test for your data?

1

2

3

4

5

6

7

1 Data Type

2 Test Purpose

3 Sample Size

4 Assumption Checks

5 Data Pairing

6 Software Tools

7 Here’s what else to consider

Statistics

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How do you choose the appropriate statistical test for your data?

1

2

3

4

5

6

7

1 Data Type

2 Test Purpose

3 Sample Size

4 Assumption Checks

5 Data Pairing

6 Software Tools

7 Here’s what else to consider

Statistics

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