#StatisticalSignificance

[academic1995]

Statistical Significance

Statistical significance is a key concept in research and data analysis, representing the likelihood that a result or relationship observed in a study is not due to random chance. In hypothesis testing, a result is considered statistically significant if the p-value falls below a pre-determined threshold (commonly 0.05), indicating strong evidence against the null hypothesis. This concept helps researchers determine the validity of their findings and ensure that conclusions drawn from data have a low probability of being due to random variation.

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Statistics is what make us understand the behavior of a population but to appreciate the behavioral patterns we need to understand the various statistical measures that are in play. Here are some of the players:

The level of statistical significance is expressed as p-value between 0 and 1. Significance Level – It is the probability of rejecting the null hypothesis when it is true. Confidence Level – It tells how sure we can be on our results if we are going to conduct further experiments. In statistical hypothesis testing, a Type I error is the rejection of a true null hypothesis, while a Type II error is the non-rejection of a false null hypothesis. T-Tests are used when we have to compare two means and have to determine if they are statistically different from each other or not. Adjusted R-Squared – It is important to understand degree of freedom (df) before we could appreciate adjusted R-Squared Coefficient of Variation – It measures the dispersion of data point around the mean. Coefficient of Correlation – It measures the strength of relative movement of two variables. Normalization and Standardization both are re-scaling techniques. Normalization adjusts the data whereas Regularization adjusts the prediction function.

#statistics,#statisticalsignificance,#confidencelevel,#statisticalhypothesis,#RSquared,#AdjustedRsquared,#Normalization,#Standardization,#Regularization,#ML,#MachineLearning,#50KeyConcepts

[innrpin]

We all use some or the other product. We have different products and we have different users. Each set of product has its own set of features. Similarly, each group of users have their own set of preferences. The sales conversion happens when there is right match between the product feature and the user group. Even if it is a right product but when offered to a wrong set of users we miss the trick. It won’t serve the purpose.

Since there are multiple product features and we have so many groups of user, the key question remains. The question of how to get that right combination to fetch the best possible result. This exercise cannot be done arbitrarily nor it can be done manually. It needs some kind of tool to test such multiple combinations to arrive at the best possible one. The test result gets clinically analyzed to prescribe the best product-user recommendation.

Hence, we need to answer two fundamental questions.

Question (1): How two product features are different?

Question (2): How to two group of users are different?

#ABtesting,#Cohortanalysis,#campaign,#statisticalsignificance,#hypothesistesting,#salesconversion,#productuser,#productfeature,#usergroups,#makeupandbreakup,#digitalmarketing