Serenity is the space that allows us to breathe and let go
Attribution Errors
Attribution errors, also known as attribution biases, are cognitive biases that affect how individuals interpret and explain the behavior of themselves and others. These biases involve making inaccurate or biased judgments about the causes of behaviors, often by attributing them to dispositional (internal) factors or situational (external) factors. One common attribution error is the fundamental attribution error (FAE), which occurs when people tend to overemphasize dispositional factors and underestimate situational factors when explaining the behavior of others. For instance, if someone witnesses a colleague being late to work, they might attribute it to the colleague's laziness or lack of punctuality (dispositional), while ignoring the possibility that the colleague might have encountered traffic or had an emergency (situational).
Another attribution error is the actor-observer bias, which relates to the tendency for individuals to attribute their own behavior to situational factors (e.g., "I was late because of traffic") but attribute the behavior of others to dispositional factors (e.g., "They were late because they're always irresponsible"). This bias highlights the differing perspectives people have when explaining their own actions versus the actions of others, often giving themselves the benefit of the doubt while judging others more critically. Understanding attribution errors is essential because they can lead to misunderstandings and conflicts in interpersonal relationships and can affect how individuals perceive and interact with others. Recognizing these biases can help people become more empathetic and make more accurate judgments about the behaviors and motivations of those around them.
Another attribution error is the actor-observer bias, which relates to the tendency for individuals to attribute their own behavior to situational factors (e.g., "I was late because of traffic") but attribute the behavior of others to dispositional factors (e.g., "They were late because they're always irresponsible"). This bias highlights the differing perspectives people have when explaining their own actions versus the actions of others, often giving themselves the benefit of the doubt while judging others more critically. Understanding attribution errors is essential because they can lead to misunderstandings and conflicts in interpersonal relationships and can affect how individuals perceive and interact with others. Recognizing these biases can help people become more empathetic and make more accurate judgments about the behaviors and motivations of those around them.
Compassion is the heart's way of responding to the suffering of others
Towards a more Humane Society. Contemplating an emotion, 1 line a day. Our divided and conflicted world needs compassion more than ever. #MentalHealth.
Compassion is the heart's way of responding to the suffering of others.
Creativity is the key to unlocking the doors of the unknown
Creativity is the key to unlocking the doors of the unknown
The Problem with DEI
DEI initiatives often assume that if you open the door, targeted people will automatically rush in. But these targeted population were told for so long that this door did not belong to them, or that its too hard for them, you are not intelligent /capable enough; if this has been the messaging of the last 100 years, there is going to be unconscious bias, where part of you starts believing this to be true (like when women are told prior to a math test, that women are bad in math, they end up doing worse on the test than if not told that info).
So the initiative has to be both opening the door and also nudges from other end saying, you can do this, we welcome you and will work to support you, demystifying the process of what the door is and how to go about even approaching that door.
So the initiative has to be both opening the door and also nudges from other end saying, you can do this, we welcome you and will work to support you, demystifying the process of what the door is and how to go about even approaching that door.
And why don't DEI initiatives automatically include disability or when disability is included, its often as an afterthought when it's pointed out that it's probably not PC to exclude. Why is disability not recognized as a very very historically marginalized group at the onset. The fact of a childhood disability for instance means the exclusion starts in childhood itself which perpetuates and intensifies the exclusion as you age. (Somehow the fact of disability transcends even the color of your skin and you are pushed to the bottom of the food chain).
p-value goes knock knock
Knock, knock.
Who's there?
Who's there?
P-value.
P-value who?
P-value less than 0.05, and I'm statistically significant enough to knock your null hypothesis out of the park!
P-value who?
P-value less than 0.05, and I'm statistically significant enough to knock your null hypothesis out of the park!
-Hari Srinivasan
p-value
p-value for the layman
What is a p-value?
At its core, a p-value is a number that helps us determine the significance of an observation or result in statistical analysis. Imagine you've conducted an experiment or a survey, and you want to know if your findings are meaningful or just a result of chance. The p-value is your guide.
The Role of Probability
To grasp p-values, you need to understand the concept of probability. Think of it as a measure of how likely something is to happen. In statistics, we often want to know the probability of observing certain data if there's no real effect or difference. This is where p-values come into play.
Hypotheses: The Foundation
In any scientific study, you start with two hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis represents the idea that there's no significant effect or difference, while the alternative hypothesis suggests the opposite – that there is a significant effect or difference.
The Experiment and the Data
You gather your data, conduct your analysis, and calculate a test statistic, which quantifies the difference between your observed data and what you would expect under the null hypothesis. This test statistic follows a particular distribution, like the normal distribution for many common statistical tests.
The P-Value's Revelation
Here's the moment of truth: the p-value tells you the probability of obtaining a test statistic as extreme as, or more extreme than, the one you calculated if the null hypothesis is true. In simpler terms, it answers the question: "How likely is it that my observed results are just due to random chance?"
Interpreting P-Values
Now, the key interpretation comes into play. If your p-value is small, typically less than 0.05 (but it can vary depending on the field), it suggests that your observed results are unlikely to have occurred by chance alone. This is your green light to reject the null hypothesis and accept that you've found something significant.
Conversely, if your p-value is large (greater than 0.05), it indicates that your observed results are quite likely to be explained by random chance, and you should stick with the null hypothesis.
It's Not Absolute Proof
One crucial thing to understand is that p-values don't provide absolute proof or disproof. They offer a level of evidence, but they can't tell you the size of an effect or whether it's practically meaningful. They merely guide you in determining if your results are statistically significant.
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