Data Science Course > Hypothesis Testing

Statistical Hypothesis in Data Science

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A hypothesis is frequently referred to as a "informed guess" regarding a particular parameter or population. After it has been defined, data can be gathered to see if it provides sufficient evidence to support the hypothesis.

There are three types of statistical hypotheses:

  • Null Hypothesis - This is the default assumption that there is no relationship between two variables.
  • Alternative Hypothesis - This is when there is a relationship between two variables. The alternative hypothesis may be a statement about the strength or direction of the relationship between these variables.
  • Confidence Level - This represents how likely it would be for an outcome to occur if the null and alternative hypotheses were true.

Hypothesis testing is a statistical approach used by scientists and researchers to determine the validity of their claims concerning real-world/real-life events. In statistics and data science, hypothesis testing procedures are frequently used to determine if statements regarding the recurrence of occurrences are true.

There are many benefits of using a statistical hypothesis. One of the most important benefits is that it can be used to identify patterns in data and make predictions about what will happen in the future.

A statistical hypothesis test is a statistical inference procedure that is used to determine a probable conclusion from two competing hypotheses. A null hypothesis and an alternative hypothesis for the probability distribution of the data are proposed in a statistical hypothesis test.

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Course Content

  1. Understanding of Data Science
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  2. How to use LearnVern
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  1. Introduction to Data Measurement in Data Science
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  2. Types of Variables in Data Science
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  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
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  2. Frequency Distribution in Data Science
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  3. Bar Graphs and Pie Charts in Data Science
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  4. Summarizing Quantitative Data in Data Science
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  5. Cummulative Distribution in Data science
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  6. Histogram and Scatter Plot in Data Science
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  7. Cross Tabulation in Data Science
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  1. Measures of Central Tendency and Variability in Data Science
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  2. Mean and Percentiles in Data Science
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  3. Quartiles in Data Science
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  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
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  6. Application of Standard Deviation in Data Science
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  7. Z-score and Co-efficient of Variation in Data Science
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  8. Measures of Central Tendency for Grouped Data in Data Science
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  9. Standard Deviation and Variance for Grouped Data in Data Science
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  1. Skewness in Data Science
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  2. Kurtosis in Data Science
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  3. Box and Whisker Plots in Data Science
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  1. Hypothesis Testing in Data Science
    6m 33s
  2. Statistical Hypothesis in Data Science
    6m 30s
  3. Errors in Statistical Hypothesis in Data Science
    5m 45s
  4. Statistical Inference for Single Population in Data Science
    7m 11s
  5. Testing Example in Data Science
    5m 27s
  6. Statistical Inference with Two Population in Data Science
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  1. R Programming in Data Science
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  2. Data Types in R in Data Science
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  3. Understanding R Console and R Studio in Data Science
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  1. Analysis of Variance and Annova in Data Science
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  2. Assumption and Variation in One-Way ANOVA in Data Science
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  3. Two way ANOVA in Data Science
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  4. Summary of ANOVA in Data Science
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  6. One way ANOVA Example in Data Science
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  7. Two way ANOVA Example in Data Science
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  1. Understanding Datamining in Data Science
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  1. Introduction to Simple Linear Regression and Correlation in Data Science
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  2. Least Square and Residual Analysis in Data Science
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  4. Hypothesis and Co-Efficient of Determination in Data Science
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  5. Regression Example - Creating Model in Data Science
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  6. Residual Analaysis of Model example in Data Science
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  7. Residual Analysis of Model Part 2 in Data Science
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  8. Residual Analysis of Model Part 3 in Data Science
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  1. Understanding Clustering in Data Science
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  2. Hierarchical Clustering in Data Science
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  3. Example of Hierarchial Clustering in Data Science
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  4. K Means Clustering in Data Science
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  5. Example of K Means Clustering in Data Science
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  1. Theoretical Concepts of Dashboard
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  2. Creating Pivots and Preparing Charts
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  3. Slicers in Dashboard
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  4. Preparing Final Dashboard
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  5. Presenting Dashboard
    3m 2s
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