Скачать или смотреть Which Statistical Methods are Best for Data Collection?

In this video, we break down one of the most important (and sometimes intimidating) parts of research methods—statistics in research. Whether you are working on a thesis, dissertation, or scientific study, knowing how to choose the right statistical test is essential for producing reliable and valid results.

We start with the basics: the types of data researchers often collect. It is important to learn the difference between categorical data (clear-cut groups like yes/no, male/female, high/medium/low) and continuous data (values on a spectrum, like age, weight, or test scores). Understanding this difference is crucial because it determines what kind of statistical analysis you should use.

For categorical data, simple frequency tables and the chi-square test are common. For continuous data, researchers often use scatter plots to visualize patterns, along with tests like Spearman’s correlation (for ranked or ordinal data) or Pearson’s correlation (for interval or ratio data). Also, regression analysis methods like linear, bivariate, and logistic regression allow you to dive deeper into relationships between variables.

The key takeaway? Statistics is not just math—it’s the foundation of trustworthy research. Without proper testing, you might see correlations that are really just random artifacts. With the right test, you can confidently say whether your findings reflect a true effect or not.

Whether you’re a beginner in research or brushing up on your knowledge, this video will help you understand how to pick the right statistical test, avoid overfitting, and strengthen the reliability of your study.

👉 Perfect for students, PhD researchers, and anyone learning research methods, biostatistics, or data analysis in academia. Hit that like button and subscribe for more such educational content!

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