Question

Is there a statistically significant difference in weight between men and women? (Conduct an independent t test.)

Identify the independent and dependent variables.
Write a null hypothesis.
Write an alternative non-directional (2-tail) hypothesis.
Interpret your results. Guidelines for interpreting independent t tests can be found in What to Include When Writing Up Independent t Test Results (PDF).

Answer 1

Is there a statistically significant difference in weight between men and women? (Conduct an independent t test.)

If the characteristics of a population under observation differs for group members, it is called a variable. It can be classified as quantitative or qualitative (also known as categorical) variables.

a. Dependent Variable: Weight

It is the observable and measurable factor and determines the effect of the independent variable. The dependent variable can be the participant’s response.

In this case the dependent variable is

b. Independent variable: Men & Women

An independent variable is a variable that is believed to be affecting the dependent variable.

c. Write a null hypothesis(H0):

There will not be any significant difference in weight between women and men.

The null hypothesis states that there will not be any relationship between the two variables being studied. It states that the results are due to chance.

d. Write an alternative non-directional (2-tail) hypothesis (H1): There will be a significant difference in weight between women and men.

A non-directional hypothesis in a study that compares the performance of two groups, and the researcher will not state which group she believes perform better. In other words the two-tailed non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified. Most of the researchers use non-directional hypotheses.

e. Interpret your results.

Guidelines for interpreting independent t tests can be found in : What to Include When Writing Up Independent t Test Results (PDF).

The independent-samples t-test (or independent t-test, or students t-test) compares the means between two independent variables on the same dependent variable.

The null hypothesis for the independent t-test is that population means from the two unrelated groups or the independent variables are equal:

H₀: m₁ = m₂

In the alternative hypothesis the population means are not equal:

H₁: m₁ ≠ m₂

OR

H0: m1 - m2 = 0 (the difference between the two population means is equal to 0)

H1: m1 - m2 ≠ 0 (the difference between the two population means is not 0)

where:

m1 is the mean of women’s weight and

m2 is the mean of men’s weight

To reject or accept the alternative hypothesis, we have to set a significance level (alpha , P) that allows us to either reject or accept the hypothesis. Usually this value is set at 0.05

Degrees Of Freedom (df)

As we are working with two independent groups, restrict one df to the mean for each group. Therefore, df for an independent-samples t test will be (n1 – 1) + (n2 –1), where n1 and n2 are the sample sizes for each of the independent groups, respectively.

Or N – 2, where N is the total sample size for the study.

• Assumption of homogeneity of variance can be tested using Levene's Test of Equality of Variances ; as the independent t-test assumes the variances of the two groups you are measuring are equal in the population.

Reporting:

An independent-samples t-test was conducted to compare the weight women and men.

• One-sample t(df) = t-value, p = p-value

• Two-sample t(df) = t-value, p = p-value

• paired t(df) = t-value, p = p-value