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.

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.