What's wrong with running a 2-way ANOVA where one factor is sex and the other factor is degree of athleticism? If you wanted to, you could actually use two t-tests. You would, however, have to apply a Bonferroni (or similar) correction to the alpha level.

As said, the basic analysis should be the 2-way ANOVA (sex and athleticism as factors), which will give you the effect of gender (i.e. are there differences in general across gender), the effect of athleticism (i.e. are there differences between athletic and non-athletic adolescents), and the interaction of the two factors. You can further explore with individual t-tests.

Typical procedure when you have 2 or dependent variables would be a Multivariate Analysis of Variance (MANOVA), which serves as an omnibus to minimize a Type 1 error.

I proceeded with one way anova. Is it wrong ?.

Using an ANOVA or univariate procedure is analyzing each dependent variable in a vacuum--as if they don't interact or effect each other--which is physiologically rare in the human body. So you are enhancing the opportunity for Type I and Type II errors. So your are essential creating a Type III error--using inappropriate stats. You need to first determine if a significant main effect occurred in whatever independent variable you are testing such as gender. If a significant main effect is observed using MANOVA, then the univariate stats that automatically follow will tell you which (male or female) response significantly increased or decreased. If there is no significant main effect, your analysis is over--you cannot use the univariate/ANOVA findings. Thus, a MANOVA will look at gender, athleticism, and gender x athleticism interaction to minimize a Type I error.

I have applied MANOVA. I got that there was no significant interaction effect between gender and athleticism. on combined dependent variables as p=0.21 for Wilks' lambda. But p=.000 was observed for gender and athleticism separately. So, can I report that the combined dependent variables were significantly different between athletic and non- athletic groups and also between males and females.

Not exactly. You can say there were significant main effects by gender (....) and athleticism (.....).

Then, you have to look at the post hoc univariate tests following the MANOVA to see which of the the dependent variables are significant and which are not--be sure to report both. Download some of my publications, read the results sections, and see how it would be written. Also note the information at the bottom of tables. Good luck in your research.

And, naturally, you'll have to explain in the Discussion section why certain variables were significant--but just as important--why certain variables were not. Reporting non-significance is a good thing too.

There, you have it!

Sir @Michael C. Meyers, thank you for everything suggested me. Now I am on performing the post hoc tests for gender and athletecism one by one but as bothe variables have only two categories each (male and female; athletic and non-athletic), poat hoc tests are not being performed. They are available only for three or more groups. Online sources are suggesting me that estimated marginal means will do in this case. For̥. e.g I am getting that Height of males is significantly greater than females (p

The phrase "post hoc" simply means "after the event"--which is ANY test following MANOVA that you choose to use. That is why I said to look at the post hoc or univariate test that follows--which typically is estimated marginal means in SPSS or SAS. You should see your dependent variables on the left, and a "Sig" column approximately 3 columns to the right---then check to see which DVs were significant and which were not.

Thank you so much sir Michael and everybody else. I learnt a lot from you.

You are more than welcome. Have a great week.