what is inference and ratio in quantitative?

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 In the context of quantitative variables, "inference" refers to the process of drawing conclusions or making predictions about a population based on sample data. When conducting inferential statistics, researchers use sample data to infer or estimate population parameters, such as means, proportions, or correlations. This process involves making generalizations or predictions about the larger group (population) from which the sample was drawn.

On the other hand, "ratio" in quantitative variables refers to the level of measurement. Ratio variables have a meaningful zero point and equal intervals between values. This means that not only can we determine the order of values, but we can also calculate meaningful ratios between them. Common examples of ratio variables include measurements such as height, weight, time, and income.

Understanding both inference and ratio variables is crucial in quantitative research and statistical analysis. Inference allows researchers to make predictions or draw conclusions about populations based on sample data, while ratio variables provide precise measurements with meaningful zero points, facilitating accurate comparisons and calculations.


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