Varians Sampel Kalkulator

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What Is Sample Variance?

Sample variance, written s², is what you calculate when your numbers are a sample from a bigger group and you want to estimate how spread out that bigger group is. A survey of 200 customers, ten bolts measured from a day's production, or eight volunteers in a pilot study are all samples. The calculation is the same as for population variance except for the last step: you divide the sum of squared deviations by n − 1 instead of n. That small change, called Bessel's correction, stops the result from systematically underestimating the true spread. This page explains when to use it, why it works and how to calculate it by hand.

Worked Example: Task Completion Times

A manager times 8 employees, chosen at random from a team of 200, on the same task. The 8 times are a sample, so the variance of the whole team is estimated with s².

Data (minutes):12, 15, 11, 14, 13, 16, 9, 14

  1. Mean. x̄ = 104 / 8 = 13
  2. Deviations and squares. Subtract the mean from each value and square the result:
xᵢxᵢ − x̄(xᵢ − x̄)²
12−11
1524
11−24
1411
1300
1639
9−416
1411
Sum036
  1. Sum of squares. Σ(xᵢ − x̄)² = 36
  2. Sample variance. s² = 36 / (8 − 1) = 5.1429; sample standard deviation s = √5.1429 = 2.2678

Sample variance s² = 36 / 7 ≈ 5.143 square minutes, so the sample standard deviation is s ≈ 2.268 minutes. Dividing by 8 instead would give 4.5, which would understate the spread of the whole team.

Formula

Sample variance uses the sample mean x̄ and divides by n − 1:

Sample Variance

s² = Σ(xᵢ − x̄)² / (n − 1)
  • s²Sample variance
  • xᵢEach value in the sample
  • x̄Sample mean
  • n − 1Sample size minus one (degrees of freedom)

Sample Standard Deviation

s = √s²
  • sSample standard deviation, in the original units

How to Calculate Sample Variance Step by Step

  1. Check that you have a sample.Your data should be a subset of the group you want to describe. If it is the whole group, use population variance instead.
  2. Find the sample mean.x̄ = Σxᵢ / n.
  3. Subtract the mean from each value.These deviations always add up to zero.
  4. Square and add the deviations.This is the sum of squares, Σ(xᵢ − x̄)².
  5. Divide by n − 1.For 8 values divide by 7, for 30 values divide by 29, and so on.
  6. Take the square root if you need s.The sample standard deviation is in the same units as the data.

More Sample Variance Examples

1Four Measurements From a Batch

Data:3, 7, 7, 19
Solution:Mean = 9. Deviations: −6, −2, −2, 10. Squared: 36, 4, 4, 100. Sum of squares = 144.
Answer:Sample variance = 144 / 3 = 48, s ≈ 6.928

2Survey: Hours of Sleep (5 respondents)

Data:7, 6, 8, 5, 9
Solution:Mean = 7. Deviations: 0, −1, 1, −2, 2. Squared: 0, 1, 1, 4, 4. Sum of squares = 10.
Answer:Sample variance = 10 / 4 = 2.5, s ≈ 1.581

3Quality Check: Bottle Fill Volumes (ml)

Data:498, 502, 500, 497, 503
Solution:Mean = 500. Deviations: −2, 2, 0, −3, 3. Squared: 4, 4, 0, 9, 9. Sum of squares = 26.
Answer:Sample variance = 26 / 4 = 6.5, s ≈ 2.550

How to Use This Sample Variance Calculator

Make sure the calculator is set to Sample, then:

  1. Enter your sample values, separated by commas, spaces or line breaks.
  2. Check that the result card says Sample Variance (s²). You need at least two values.
  3. Read s², the sample standard deviation s and the sample mean.
  4. Open the calculation steps to see the n − 1 division written out with your numbers.

What the Calculator Shows You

  • The sample variance and sample standard deviation with the n − 1 divisor applied.
  • The population figure for the same data, so you can see how much the correction changes the answer.
  • Every deviation and squared deviation, useful for homework or checking a spreadsheet.
  • Calculations run in your browser; your numbers are not sent anywhere.

When to Use Sample Variance

Surveys

Responses from some customers, voters or students, used to estimate the whole group.

Experiments and clinical trials

Participants are a sample of the people the treatment is meant for.

Quality control

Measuring a few items from a production run to judge the whole run.

A/B tests

Visitors in each variant are a sample of all future visitors.

Pilot studies

A small first sample used to estimate variance when planning a larger study.

Inferential statistics

t-tests, confidence intervals and ANOVA all use s², not σ².

Why Dividing by n − 1 Works: A Small Demonstration

You can see the bias, and the fix, with a population of just three numbers.

The population

Take the population {2, 4, 6}. Its mean is 4 and its population variance is σ² = 8 / 3 ≈ 2.667.

Every possible sample of two

Drawing two values with replacement gives 9 equally likely samples. For a sample of two, the sum of squared deviations is (a − b)² / 2. Three samples have a gap of 0, four have a gap of 2 and two have a gap of 4.

Dividing by n

Dividing each sample's sum of squares by n = 2 gives values whose average over the 9 samples comes out at 1.333, only half of the true 2.667.

Dividing by n − 1

Dividing by n − 1 = 1 instead gives values whose average over the 9 samples comes out at exactly 2.667, the true population variance. That is what "unbiased" means.

Why it happens

Each sample's mean sits in the middle of that sample, closer to its values than the true mean usually is. Measuring from it shrinks the squared deviations, and n − 1 makes up the difference.

Soalan Lazim: Varians Sampel Kalkulator

Semua yang anda perlu tahu tentang varians.

Apakah varians sampel?

Varians sampel (s²) mengukur cara penyebaran nilai dalam sampel yang diambil daripada populasi yang lebih besar. Formulanya ialah s² = Σ(xᵢ − x̄)² / (n − 1). Ia membahagi dengan n−1 (bukan n) untuk memberikan anggaran tidak berat sebelah bagi varians populasi sebenar. Pembetulan ini — dipanggil pembetulan Bessel — mengimbangi penggunaan min sampel dan bukannya min populasi sebenar.

Bagaimana untuk mengira varians sampel langkah demi langkah?

Untuk mengira varians sampel: 1. Cari min sampel: x̄ = Σxᵢ / n. 2. Tolak min daripada setiap nilai: (xᵢ − x̄). 3. Kuadratkan setiap sisihan: (xᵢ − x̄)². 4. Jumlahkan semua sisihan kuasa dua: Σ(xᵢ − x̄)². 5. Bahagi dengan n − 1 (bukan n): s² = Σ(xᵢ − x̄)² / (n − 1). N−1 adalah penting — itulah yang menjadikan varians sampel ini dan bukannya varians populasi.

Mengapa varians sampel membahagi dengan n−1 dan bukannya n?

Membahagi dengan n−1 dipanggil Pembetulan Bessel. Apabila anda mengira varians daripada sampel, min sampel cenderung lebih dekat dengan titik data sampel daripada purata populasi sebenar. Ini secara sistematik meremehkan varians. Membahagi dengan n−1 membetulkan bias ini, menghasilkan penganggar tidak berat sebelah bagi varians populasi. Nombor n−1 mewakili darjah kebebasan — anda kehilangan satu darjah kerana min mengekang data.

Bilakah saya harus menggunakan varians sampel vs varians populasi?

Gunakan varians sampel (s²) apabila data anda ialah subset yang diambil daripada populasi yang lebih besar — ​​ini termasuk tinjauan, percubaan, ujian klinikal dan kebanyakan senario dunia nyata. Gunakan varians populasi (σ²) hanya apabila anda mempunyai setiap titik data daripada keseluruhan populasi — data banci, kelas lengkap pelajar atau setiap produk dalam satu kelompok. Apabila ragu-ragu, gunakan varians sampel.

Apakah varians sampel bagi 3, 7, 7, 19?

Untuk sampel 3, 7, 7, 19: Min = (3+7+7+19)/4 = 9. Sisihan: −6, −2, −2, 10. Sisihan kuasa dua: 36, 4, 4, 100. Jumlah segi empat sama = 144. Sampel) = 4. Sampel 144 / 3 = 48. Sisihan piawai sampel = √48 ≈ 6.928. Varians yang tinggi mencerminkan jurang yang besar antara 19 dan nilai lain.

Berapa banyak titik data yang saya perlukan untuk varians sampel?

Anda memerlukan sekurang-kurangnya 2 titik data untuk varians sampel. Dengan hanya 1 titik data, n−1 = 0 dan pembahagian dengan sifar tidak ditentukan. Dalam amalan, lebih banyak titik data menghasilkan anggaran varians yang lebih dipercayai. Untuk data taburan normal, 30+ pemerhatian ialah ambang biasa untuk varians sampel menjadi stabil.