The hypothesis is a statement where we create an assumption or a claim about the population parameter that we will test using the sample data collected.

In statistics, a hypothesis is just a guess or claim about a population that we test with data.

In statistics, a hypothesis is a specific, testable statement about a population parameter (such as a mean, proportion, or variance). It is an assumption or claim that we evaluate using sample data.

In statistics, a hypothesis is a clear statement or assumption about a population that we want to test using data. It’s like making a claim about how things are, and then checking if the evidence supports it.

A hypothesis in statistics is an assumption or claim about a population parameter (like the mean, proportion, or variance).

‎It’s a statement we want to test using data from a sample.

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‎ Types of Hypotheses

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‎1. Null Hypothesis (Hsub0)

‎   * The "default" or "status quo" assumption.

‎   * It usually says there is no effect, no difference, or nothing new.

‎   * Have equal sign

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‎2.Alternative Hypothesis (Hsub1)

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‎   * What we want to prove.

‎   * It says there is an effect, a difference, or a change

‎   * Have not equal sign

  In statistics, a hypothesis is basically a claim or assumption about a population value (like an average or percentage) that we test using sample data.

There are two main types: 

Null hypothesis (H₀) and  Alternative hypothesis (H₁ or Ha)

A hypothesis is a specific statement or assumption about a population parameter (such as the mean, proportion, or variance) that can be tested using sample data.

• A hypothesis is a claim or assumption about the population.
• It is tested using sample evidence.
• There are usually two types:
  • Null hypothesis (H₀): Assumes no effect or no difference.
  • Alternative hypothesis (H₁): Suggests there is an effect or difference.

🔹 Ex:

• Claim: "The average height of students in a class is 165 cm."
• Here, the hypothesis is about the population mean (165 cm).