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Here are some examples of real-world statistical analyses that don’t use p-values and significance Testing

Your results from a hypothesis test are statistically significant. Bravo! Are these results important? Not so. Significance not necessarily mean the results are practically significant in real world Statistical Analysis. <br>In this blog post, I will list out few examples of real world statistical analyses that do not use p-value and significance testing. Before that, let us understand what exactly the p-value mean? <br> <br>Statswork is one among the countryu2019s leader in providing Data Analysis Services and Statistical Consulting Services. Contact Statswork for availing our services.<br>

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Here are some examples of real-world statistical analyses that don’t use p-values and significance Testing

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  1. ResearchPaper REAL-WORLDSTATISTICAL ANALYSESWITHOUT P-VALUES ANDSIGNIFICANCE TESTING Copyright © 2019 Statswork. All rightsreserved

  2. What exactly the p-value mean? Copyright © 2019 Statswork. All rightsreserved

  3. A p-value is the probability for obtaining an effect at least asextremeas one sampledata. Statistically significant results are obtained when the strength of the evidence as the sample has passed the defined significance level(alpha). Results from a hypothesis testing procedure determines whether assumed null hypothesis is correct forpopulation. p-values and statistical significance relate to importance. But, there are situations where these may not be useful in practical world. p-value is used to determine the statistical significance in hypothesis tests such as chi-square tests, t-tests, ANOVA, and regression coefficients among many others. Copyright © 2019 Statswork. All rightsreserved

  4. LIST OF SITUATIONS SIGNIFICANCETESTING LEADS TO IMPRACTICAL RESULTS Copyright © 2019 Statswork. All rightsreserved

  5. Example1 Mr. X is evaluating a training program by comparing the test scores of participants to those who study on theirown. Condition: Difference between these groups must be at least 5 points to represent a practically meaningfu effectsize. Results: The results from the study shows a statistically significant difference with an average score of 3 points higher on a 100-pointtest. Conclusions: These results are statistically significant, the 3-point difference is less than our 5-point threshold. Study provides evidence that the effect exists, but it is too small to be meaningful in the real world.Concludedthat, the time and money that participants spend on this training program are not worth an average improvement of only 3 points. Copyright © 2019 Statswork. All rightsreserved

  6. Example2 The mean pizza deliverytime. Condition: Null hypothesis is true when there is a 3% chance. Thresholdvalueis 0.05. Results: The obtained mean delivery time is longer by 10 minutes with a p-value of 0.03. Here the p valueof 0.03 is less than the threshold 0.05 and hence we conclude it is statistically significant. Also this results will be impractical because we belief that the mean delivery time of the pizza is always 30 minutes oflesser. Conclusions: In this situation, we may think about the result from the analysis and our true belief that the delivery time is lesser or equal to 30 minutes is a valid null hypothesis. In addition, from the reviews we may conclude there is also situation late delivery has taken place. From this, one may decide not to buy any pizza from that particular shop too. Therefore, in my opinion, result based on p-value is impractical like in thissituation. Copyright © 2019 Statswork. All rightsreserved

  7. Example3 Dance of the p-value by Geoff Cumming explains the effect of p-value on varying samplesizes. If the sample mean vary among the sample, then the p- value will also vary and this effect will result in wrong conclusion based on thep-value. Copyright © 2019 Statswork. All rightsreserved

  8. Example4 There is always an interesting hypothesis to understand how the p-value fails in real timesituation. Critique of the use of significance tests for the hypothesisthat “the earth is round” withp<0.05 Amrhein et al (2017) argued and discussed for the hypothesisthat “the earth is flat" (p >0.05) Copyright © 2019 Statswork. All rightsreserved

  9. CONCLUSION Statistical testing and the resultant p-value indicates that the sample provides sufficient evidence to conclude that the effect exists in the population. However, there is always a question arises that; p-value is practically a valid measure? Thus, the use of test statistics, number of samples and framing the null hypothesis really matters in arriving any statisticalconclusion. Copyright © 2019 Statswork. All rightsreserved

  10. Statswork Lab @ Statswork.com www.statswork.com Copyright © 2019 Statswork. All rightsreserved

  11. PHONENUMBER UK INDIA :+44-1143520021 :+91-4448137070 GET IN TOUCH WITHUS EMAILADDRESS: info@statswork.com Copyright © 2019 Statswork. All rightsreserved

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