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Tag: statistics

Alternative to the t-test When Data Are Not Normally Distributed

By Kanda Data / Date Feb 09.2026 / Category Statistics

The t-test is one of the most popular methods for comparing the means of two sample groups. In practice, the t-test can be used for two paired sample groups or two independent sample groups. We can perform this difference test using a paired-samples t-test or an independent-samples t-test, depending on the characteristics of the data being analyzed.

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When Should Natural Logarithmic Data Transformation Be Applied?

By Kanda Data / Date Feb 02.2026 / Category Econometrics

When researchers, practitioners, or students are conducting data analysis on research results, they are often faced with data that do not meet the assumptions required by the chosen analytical method. After testing, it may turn out that the data distribution is highly skewed, the variance is not constant, or non-linear relationships between variables are observed. These conditions represent common challenges in statistical analysis, especially when using parametric methods such as linear regression analysis.

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Should Data Normality Testing Always Be Performed in Statistical Analysis?

By Kanda Data / Date Jan 26.2026 / Category Statistics

In statistical analysis of research results, normality testing is often treated as an analytical step that is almost always conducted before proceeding to further analysis. Many researchers, students, and data practitioners believe that without a normality test, statistical analysis results become less scientific.

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Differences in Nominal, Ordinal, Interval, and Ratio Data Measurement Scales for Research

By Kanda Data / Date Jan 23.2026 / Category Statistics

In research activities, data serve as the main foundation for analysis and drawing conclusions. However, not all data have the same characteristics. One of the common challenges faced by novice researchers is understanding and applying data measurement scales. In fact, an understanding of data scales greatly determines the type of statistical analysis that can be used as well as the validity of research results.

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Regression Analysis for Binary Categorical Dependent Variables

By Kanda Data / Date Sep 27.2025 / Category Multiple Linear Regression

When we talk about regression analysis, we often think about parametric variables measured on at least an interval or ratio scale. But what if we want to analyze the effect of independent variables on a dependent variable that happens to be categorical in nature?

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How to Sort Values from Highest to Lowest in Excel

By Kanda Data / Date Sep 01.2025 / Category Excel Tutorial for Statistics

When conducting tabulation and data analysis, we sometimes need to sort values for a specific purpose. It turns out that sorting values from the highest to the lowest, or vice versa, can easily be done using Excel. We are, of course, already familiar with Excel as a tool that we use in our daily office work, one of which is its advantage in data processing.

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How to Perform Descriptive Statistics in Excel in Under 1 Minute

By Kanda Data / Date Aug 21.2025 / Category Excel Tutorial for Statistics

Descriptive statistical analysis is essential to carry out for your research data. From the output of descriptive statistics, you can obtain information such as the minimum, maximum, mean, standard deviation, standard error, and more. These values are important to interpret for each variable observed in your research.

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How to Tabulate Data Using Pivot Table for Your Research Results

By Kanda Data / Date Aug 18.2025 / Category Excel Tutorial for Statistics

For those of you currently conducting research, the stages of data entry and data tabulation are important parts of the process. Excel, which we already use daily for data processing, can also help us perform data tabulation quickly.

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  • Alternative to the t-test When Data Are Not Normally Distributed
  • When Should Natural Logarithmic Data Transformation Be Applied?
  • Should Data Normality Testing Always Be Performed in Statistical Analysis?
  • Differences in Nominal, Ordinal, Interval, and Ratio Data Measurement Scales for Research
  • Reasons Why the R-Squared Value in Time Series Data Is Higher Than in Cross-Section Data
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