Type I and Type II Errors in Research Methodology
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The document explains type I and type II errors in hypothesis testing, emphasizing that a type I error occurs when a true null hypothesis is incorrectly rejected, leading to a false positive. Conversely, a type II error happens when a false null hypothesis is not rejected, resulting in a false negative. The document highlights the importance of controlling type I errors in research, as they are considered more serious.
Type I and Type II Errors in Research Methodology
- 1. Type I and Type II Errors Explained in Malayalam Dr. Chinchu C.
- 2. Type I Error • A Type I error is when we reject the null hypothesis when it is in fact true • In other words, it is when we wrongly assume that there is an effect when no such effect really exists • Like a False Positive result in a Covid-19 RTPCR test
- 3. Type I Error Example • Assume that we are testing to see if there is a statistically significant difference between the average marks obtained by female and transgender students in a class • Also assume that in reality, there is no such significant difference • The Null Hypothesis will be “Average Marks of Females and Average Marks of Transgender persons are not statistically different” • H0: Marks(Female) = Marks(TG) • Rejecting this Null Hypothesis would mean that we conclude that there is a significant difference in the average marks between female and transgender students. • A Type I error is when we wrongly conclude that there is such a significant difference when no such significant difference actually exists.
- 4. Type II Error • A Type II error is when we do not reject the null hypothesis when in fact we should have rejected it • In other words, it is when we conclude that there is no effect, when in reality there is an effect • Like a false negative in Covid-19 RTPCR test
- 5. Type II Error Example • Assume that we are testing to see if there is a statistically significant difference between the Average BMI of Active persons and Sedentary persons • Also assume that in reality, there exists such a significant difference • The Null Hypothesis will be “BMI of Active persons and BMI of Sedentary persons are not statistically different” • H0: BMI(Active) = BMI(Sedentary) • Failing to Reject the Null Hypothesis would mean that we conclude that there is a no significant difference in the average BMI between Active and Sedentary persons. • A Type II error is when we wrongly conclude that there is no significant difference when a difference actually exists.
- 6. Type I error is considered more serious in Research (and the one that we try to control the most)
- 7. 1000 culprits can escape, but, one innocent person should not be punished ആയിരം കുറ്റവാളികൾ രക്ഷപ്പെട്ടാലും ഒരു നിരപരാധി പപാലും ശിക്ഷിക്കപ്പെടരുത
- 8. നിരപരാധി Innocent (Null Hypothesis True) Type I Error Type II Error കുറ്റവാളി Guilty (Null Hypothesis False) ശിക്ഷിക്കുന്നു Guilty Verdict (Rejecting Null Hypothesis) പ്പവറുപ്പെ വിടുന്നു Not Guilty Verdict (Failing to Reject Null Hypothesis) Our Decision
- 9. The 𝛂 (Level of Significance) • The level of significance is commonly understood as the probability of making a Type I error. • Typically set at 5% (expressed as 0.05) to balance between the probability for Type I and Type II errors.