When are unequal sample sizes are and are not a problem?

When are unequal sample sizes are and are not a problem?

In your statistics class, your professor made a big deal about unequal sample sizes in one-way Analysis of Variance (ANOVA) for two reasons. 1. Because she was making you calculate everything by hand. Sums of squares require a different formula* if sample sizes are unequal, but statistical software will automatically use the right formula.

Can a classification problem be treated as a normal problem?

— Learning from imbalanced data – Open challenges and future directions, 2016. A slight imbalance is often not a concern, and the problem can often be treated like a normal classification predictive modeling problem. A severe imbalance of the classes can be challenging to model and may require the use of specialized techniques.

When do unequal sample sizes occur in factorial ANOVA?

Factorial ANOVA includes all those ANOVA models with more than one crossed factor. It generally involves one or more interaction terms. Real issues with unequal sample sizes do occur in factorial ANOVA in one situation: when the sample sizes are confounded in the two (or more) factors. Let’s unpack this.

Which is more important in an imbalanced classification problem?

When working with an imbalanced classification problem, the minority class is typically of the most interest. This means that a model’s skill in correctly predicting the class label or probability for the minority class is more important than the majority class or classes.

What is the statistical advantage of equal sample sizes?

Additionally, while equal-sized groups maximise statistical power, the advantage is easily overstated. An experiment with 30+30 participants has a 76% chance to detect a systematic difference of 0.7 standard deviations between the two group means; for an experiment with 20+40 participants, this probability is 71%.

Are there unequal sample sizes for mixed ANOVA?

However, all these different groups have different numbers of examinees. The first group has 490 participants, the second group has 1919 participants and the third group has 529 participants. Thus, I can say that I have unequal sample sizes for Mixed ANOVA.

What is the purpose of sampling in research?

The aim of sampling is to approximate a larger population on characteristics relevant to the research question, to be representative so that researchers can make inferences about the larger population. There are many types of sampling methods, but most sampling falls into two main categories: probability sampling, and non-probability sampling.

How are null hypothesis and sample size calculated?

Both have two categorical variables. Both count the the frequencies of the combinations of these categories. They calculate the test statistic the same way. Without getting into the math, it’s basically a comparison of the actual frequencies of the combinations with the frequencies you’d expect under the null hypothesis.

How are independent samples used in ANOVA?

Independent samples t-tests are essentially a simplificiation of a one-way ANOVA for only two groups. In fact, if you run your t-test as an ANOVA, you’ll get the same p-value. And the between-groups F statistic will be the square of the t statistic you got in your t-test.

How does sample size affect the validity of a study?

Very small samples undermine the internal and external validity of a study. Very large samples tend to transform small differences into statistically significant differences – even when they are clinically insignificant.

What are the factors that affect sample size?

FACTORS THAT AFFECT SAMPLE SIZE The purpose of estimating the appropriate sample size is to produce studies capable of detecting clinically relevant differences. Bearing this point in mind, there are different formulas to calculate sample size.2,3These formulas comprise

How to calculate Sample Size for repeated measurements?

Unlike studies with independent observations, repeated measurements taken from the same participant are correlated, and the correlations must be accounted for in calculating the appropriate sample size. Some current software packages used for sample size calculations are based on oversimplified assumptions about correlation patterns.

Why is it important to choose the right sample size?

Choosing the right sample size increases the chance of detecting an effect, and ensures that the study is both ethical and cost-effective. Repeated measures designs are widely used because they have advantages over cross-sectional designs.

How to do statistical analysis of observational data?

Adding various legal and extralegal factors to a baseline model including only indicators of race and ethnicity (black, Hispanic, Native American, and Asian, with white as the comparison group), they examine changes in the log-odds coefficient for each indicator.

How are sample sizes simulated in a MA study?

In MA of homogeneous and heterogeneous studies, the simulated data were based on three forms of the sample sizes designs: equal, moderately unequal and highly unequal sample sizes, such that the total sample size is the same. First, equal numbers of subjects were assigned to each MA studies.

How is a discriminant analysis different from a regression analysis?

In many ways, discriminant analysis parallels multiple regression analysis. The main difference between these two techniques is that regression analysis deals with a continuous dependent variable, while discriminant analysis must have a discrete dependent variable.

When is power based on the smallest sample size?

Power is based on the smallest sample size, so while it doesn’t hurt power to have more observations in the larger group, it doesn’t help either. So if you have a specific number of individuals to randomly assign to groups, you’ll have the most power if you assign them equally.

How are sample sizes related to the power of an experiment?

Table 1: Comparison of the power of an experiment using complete randomisation (equal sample sizes) and the average power of an experiment using simple randomisation (possibly unequal sample sizes). The R code for the comparison is at the bottom of this post.

How to calculate the effect size of a test group?

The “test” group has about 10 states and 500 observations. Initially, I found group means + pooled standard deviation, which I used to calculate an Effect Size. Then I used a package called pwr in R and found that I needed a minimum sample size of about 135 observations per group, given .05 significance and .8 power.

What is the sum of squares for diet and exercise?

For the data in Table 15.6. 4, the sum of squares for Diet is 390.625, the sum of squares for Exercise is 180.625, and the sum of squares confounded between these two factors is 819.375 (the calculation of this value is beyond the scope of this introductory text).

How to do two independent sample unequal variance?

Two Independent Samples Unequal Variance (Welch’s Test) To conduct a two independent sample comparison of means test, you follow very similar steps as described in the one sample test with some modifications. For this problem, we want to compare the average weights of blue crabs in two river basins: (1) Tar-Pamlico and (2) Neuse River.

How to conduct two independent sample comparison of means?

To conduct a two independent sample comparison of means test, you follow very similar steps as described in the one sample test with some modifications. For this problem, we want to compare the average weights of blue crabs in two river basins: (1) Tar-Pamlico and (2) Neuse River.

When do you use the word equal size?

Use ‘of equal size’ after the noun in concern. There is a desert with two water pools of equal size. Break the dough into 15 portions of equal size (COCA). The words ‘equal-size’ is also possible but less frequently shown. Finally, ‘equally-sized’ is an improper use of those words.

Is there something wrong with comparing groups of different sizes?

In this blog post, I want to dispel a myth that’s reasonably common among students: the notion that there’s something wrong about a study that compares groups of different sizes. There is something aesthetically pleasing about studies that compare two equal-sized groups.

How to use a categorical predictor in statistics?

Statistics Total Variable Factor Count Mean Response A 4 2.000 B 4 4.500 C 4 8.500 Choose Stat > Regression > Regression > Fit Regression Model. In Responses, enter Response. In Categorical predictors, enter Factor. Click Coding. Under Reference level, choose C. Click OK in each dialog.

How are count models different from exposure models?

Count models account for these differences by including the log of the exposure variable in model with coefficient constrained to be one. The use of exposure is superior in many instances to analyzing rates as response variables because it makes use of the correct probability distributions.